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Mathematics and Statistics Theses and Dissertations
Theses/dissertations from 2022 2022.
New Developments in Statistical Optimal Designs for Physical and Computer Experiments , Damola M. Akinlana
Advances and Applications of Optimal Polynomial Approximants , Raymond Centner
Data-Driven Analytical Predictive Modeling for Pancreatic Cancer, Financial & Social Systems , Aditya Chakraborty
On Simultaneous Similarity of d-tuples of Commuting Square Matrices , Corey Connelly
Symbolic Computation of Lump Solutions to a Combined (2+1)-dimensional Nonlinear Evolution Equation , Jingwei He
Boundary behavior of analytic functions and Approximation Theory , Spyros Pasias
Stability Analysis of Delay-Driven Coupled Cantilevers Using the Lambert W-Function , Daniel Siebel-Cortopassi
A Functional Optimization Approach to Stochastic Process Sampling , Ryan Matthew Thurman
Theses/Dissertations from 2021 2021
Riemann-Hilbert Problems for Nonlocal Reverse-Time Nonlinear Second-order and Fourth-order AKNS Systems of Multiple Components and Exact Soliton Solutions , Alle Adjiri
Zeros of Harmonic Polynomials and Related Applications , Azizah Alrajhi
Combination of Time Series Analysis and Sentiment Analysis for Stock Market Forecasting , Hsiao-Chuan Chou
Uncertainty Quantification in Deep and Statistical Learning with applications in Bio-Medical Image Analysis , K. Ruwani M. Fernando
Data-Driven Analytical Modeling of Multiple Myeloma Cancer, U.S. Crop Production and Monitoring Process , Lohuwa Mamudu
Long-time Asymptotics for mKdV Type Reduced Equations of the AKNS Hierarchy in Weighted L 2 Sobolev Spaces , Fudong Wang
Online and Adjusted Human Activities Recognition with Statistical Learning , Yanjia Zhang
Theses/Dissertations from 2020 2020
Bayesian Reliability Analysis of The Power Law Process and Statistical Modeling of Computer and Network Vulnerabilities with Cybersecurity Application , Freeh N. Alenezi
Discrete Models and Algorithms for Analyzing DNA Rearrangements , Jasper Braun
Bayesian Reliability Analysis for Optical Media Using Accelerated Degradation Test Data , Kun Bu
On the p(x)-Laplace equation in Carnot groups , Robert D. Freeman
Clustering methods for gene expression data of Oxytricha trifallax , Kyle Houfek
Gradient Boosting for Survival Analysis with Applications in Oncology , Nam Phuong Nguyen
Global and Stochastic Dynamics of Diffusive Hindmarsh-Rose Equations in Neurodynamics , Chi Phan
Restricted Isometric Projections for Differentiable Manifolds and Applications , Vasile Pop
On Some Problems on Polynomial Interpolation in Several Variables , Brian Jon Tuesink
Numerical Study of Gap Distributions in Determinantal Point Process on Low Dimensional Spheres: L -Ensemble of O ( n ) Model Type for n = 2 and n = 3 , Xiankui Yang
Non-Associative Algebraic Structures in Knot Theory , Emanuele Zappala
Theses/Dissertations from 2019 2019
Field Quantization for Radiative Decay of Plasmons in Finite and Infinite Geometries , Maryam Bagherian
Probabilistic Modeling of Democracy, Corruption, Hemophilia A and Prediabetes Data , A. K. M. Raquibul Bashar
Generalized Derivations of Ternary Lie Algebras and n-BiHom-Lie Algebras , Amine Ben Abdeljelil
Fractional Random Weighted Bootstrapping for Classification on Imbalanced Data with Ensemble Decision Tree Methods , Sean Charles Carter
Hierarchical Self-Assembly and Substitution Rules , Daniel Alejandro Cruz
Statistical Learning of Biomedical Non-Stationary Signals and Quality of Life Modeling , Mahdi Goudarzi
Probabilistic and Statistical Prediction Models for Alzheimer’s Disease and Statistical Analysis of Global Warming , Maryam Ibrahim Habadi
Essays on Time Series and Machine Learning Techniques for Risk Management , Michael Kotarinos
The Systems of Post and Post Algebras: A Demonstration of an Obvious Fact , Daviel Leyva
Reconstruction of Radar Images by Using Spherical Mean and Regular Radon Transforms , Ozan Pirbudak
Analyses of Unorthodox Overlapping Gene Segments in Oxytricha Trifallax , Shannon Stich
An Optimal Medium-Strength Regularity Algorithm for 3-uniform Hypergraphs , John Theado
Power Graphs of Quasigroups , DayVon L. Walker
Theses/Dissertations from 2018 2018
Groups Generated by Automata Arising from Transformations of the Boundaries of Rooted Trees , Elsayed Ahmed
Non-equilibrium Phase Transitions in Interacting Diffusions , Wael Al-Sawai
A Hybrid Dynamic Modeling of Time-to-event Processes and Applications , Emmanuel A. Appiah
Lump Solutions and Riemann-Hilbert Approach to Soliton Equations , Sumayah A. Batwa
Developing a Model to Predict Prevalence of Compulsive Behavior in Individuals with OCD , Lindsay D. Fields
Generalizations of Quandles and their cohomologies , Matthew J. Green
Hamiltonian structures and Riemann-Hilbert problems of integrable systems , Xiang Gu
Optimal Latin Hypercube Designs for Computer Experiments Based on Multiple Objectives , Ruizhe Hou
Human Activity Recognition Based on Transfer Learning , Jinyong Pang
Signal Detection of Adverse Drug Reaction using the Adverse Event Reporting System: Literature Review and Novel Methods , Minh H. Pham
Statistical Analysis and Modeling of Cyber Security and Health Sciences , Nawa Raj Pokhrel
Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems , Zheni Svetoslavova Stefanova
Orthogonal Polynomials With Respect to the Measure Supported Over the Whole Complex Plane , Meng Yang
Theses/Dissertations from 2017 2017
Modeling in Finance and Insurance With Levy-It'o Driven Dynamic Processes under Semi Markov-type Switching Regimes and Time Domains , Patrick Armand Assonken Tonfack
Prevalence of Typical Images in High School Geometry Textbooks , Megan N. Cannon
On Extending Hansel's Theorem to Hypergraphs , Gregory Sutton Churchill
Contributions to Quandle Theory: A Study of f-Quandles, Extensions, and Cohomology , Indu Rasika U. Churchill
Linear Extremal Problems in the Hardy Space H p for 0 p , Robert Christopher Connelly
Statistical Analysis and Modeling of Ovarian and Breast Cancer , Muditha V. Devamitta Perera
Statistical Analysis and Modeling of Stomach Cancer Data , Chao Gao
Structural Analysis of Poloidal and Toroidal Plasmons and Fields of Multilayer Nanorings , Kumar Vijay Garapati
Dynamics of Multicultural Social Networks , Kristina B. Hilton
Cybersecurity: Stochastic Analysis and Modelling of Vulnerabilities to Determine the Network Security and Attackers Behavior , Pubudu Kalpani Kaluarachchi
Generalized D-Kaup-Newell integrable systems and their integrable couplings and Darboux transformations , Morgan Ashley McAnally
Patterns in Words Related to DNA Rearrangements , Lukas Nabergall
Time Series Online Empirical Bayesian Kernel Density Segmentation: Applications in Real Time Activity Recognition Using Smartphone Accelerometer , Shuang Na
Schreier Graphs of Thompson's Group T , Allen Pennington
Cybersecurity: Probabilistic Behavior of Vulnerability and Life Cycle , Sasith Maduranga Rajasooriya
Bayesian Artificial Neural Networks in Health and Cybersecurity , Hansapani Sarasepa Rodrigo
Real-time Classification of Biomedical Signals, Parkinson’s Analytical Model , Abolfazl Saghafi
Lump, complexiton and algebro-geometric solutions to soliton equations , Yuan Zhou
Theses/Dissertations from 2016 2016
A Statistical Analysis of Hurricanes in the Atlantic Basin and Sinkholes in Florida , Joy Marie D'andrea
Statistical Analysis of a Risk Factor in Finance and Environmental Models for Belize , Sherlene Enriquez-Savery
Putnam's Inequality and Analytic Content in the Bergman Space , Matthew Fleeman
On the Number of Colors in Quandle Knot Colorings , Jeremy William Kerr
Statistical Modeling of Carbon Dioxide and Cluster Analysis of Time Dependent Information: Lag Target Time Series Clustering, Multi-Factor Time Series Clustering, and Multi-Level Time Series Clustering , Doo Young Kim
Some Results Concerning Permutation Polynomials over Finite Fields , Stephen Lappano
Hamiltonian Formulations and Symmetry Constraints of Soliton Hierarchies of (1+1)-Dimensional Nonlinear Evolution Equations , Solomon Manukure
Modeling and Survival Analysis of Breast Cancer: A Statistical, Artificial Neural Network, and Decision Tree Approach , Venkateswara Rao Mudunuru
Generalized Phase Retrieval: Isometries in Vector Spaces , Josiah Park
Leonard Systems and their Friends , Jonathan Spiewak
Resonant Solutions to (3+1)-dimensional Bilinear Differential Equations , Yue Sun
Statistical Analysis and Modeling Health Data: A Longitudinal Study , Bhikhari Prasad Tharu
Global Attractors and Random Attractors of Reaction-Diffusion Systems , Junyi Tu
Time Dependent Kernel Density Estimation: A New Parameter Estimation Algorithm, Applications in Time Series Classification and Clustering , Xing Wang
On Spectral Properties of Single Layer Potentials , Seyed Zoalroshd
Theses/Dissertations from 2015 2015
Analysis of Rheumatoid Arthritis Data using Logistic Regression and Penalized Approach , Wei Chen
Active Tile Self-assembly and Simulations of Computational Systems , Daria Karpenko
Nearest Neighbor Foreign Exchange Rate Forecasting with Mahalanobis Distance , Vindya Kumari Pathirana
Statistical Learning with Artificial Neural Network Applied to Health and Environmental Data , Taysseer Sharaf
Radial Versus Othogonal and Minimal Projections onto Hyperplanes in l_4^3 , Richard Alan Warner
Ensemble Learning Method on Machine Maintenance Data , Xiaochuang Zhao
Theses/Dissertations from 2014 2014
Properties of Graphs Used to Model DNA Recombination , Ryan Arredondo
Recursive Methods in Number Theory, Combinatorial Graph Theory, and Probability , Jonathan Burns
On the Classification of Groups Generated by Automata with 4 States over a 2-Letter Alphabet , Louis Caponi
Statistical Analysis, Modeling, and Algorithms for Pharmaceutical and Cancer Systems , Bong-Jin Choi
Topological Data Analysis of Properties of Four-Regular Rigid Vertex Graphs , Grant Mcneil Conine
Trend Analysis and Modeling of Health and Environmental Data: Joinpoint and Functional Approach , Ram C. Kafle
A Maximum Principle in the Engel Group , James Klinedinst
Stochastic Modeling and Analysis of Energy Commodity Spot Price Processes , Olusegun Michael Otunuga
Theses/Dissertations from 2013 2013
Statistical Topics Applied to Pressure and Temperature Readings in the Gulf of Mexico , Malena Kathleen Allison
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The Beginner's Guide to Statistical Analysis | 5 Steps & Examples
Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.
To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.
After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.
This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.
Table of contents
Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results.
To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.
Writing statistical hypotheses
The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.
A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.
While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.
- Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
- Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
- Null hypothesis: Parental income and GPA have no relationship with each other in college students.
- Alternative hypothesis: Parental income and GPA are positively correlated in college students.
Planning your research design
A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.
First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.
- In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
- In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
- In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.
Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.
- In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
- In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
- In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
- Experimental
- Correlational
First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.
In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.
Measuring variables
When planning a research design, you should operationalize your variables and decide exactly how you will measure them.
For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:
- Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
- Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).
Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.
Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.
In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.
In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.
Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.
Sampling for statistical analysis
There are two main approaches to selecting a sample.
- Probability sampling: every member of the population has a chance of being selected for the study through random selection.
- Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.
In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.
But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.
If you want to use parametric tests for non-probability samples, you have to make the case that:
- your sample is representative of the population you’re generalizing your findings to.
- your sample lacks systematic bias.
Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.
If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .
Create an appropriate sampling procedure
Based on the resources available for your research, decide on how you’ll recruit participants.
- Will you have resources to advertise your study widely, including outside of your university setting?
- Will you have the means to recruit a diverse sample that represents a broad population?
- Do you have time to contact and follow up with members of hard-to-reach groups?
Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.
Calculate sufficient sample size
Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.
There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.
To use these calculators, you have to understand and input these key components:
- Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
- Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
- Expected effect size : a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
- Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.
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Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.
Inspect your data
There are various ways to inspect your data, including the following:
- Organizing data from each variable in frequency distribution tables .
- Displaying data from a key variable in a bar chart to view the distribution of responses.
- Visualizing the relationship between two variables using a scatter plot .
By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.
A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.
In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.
Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.
Calculate measures of central tendency
Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:
- Mode : the most popular response or value in the data set.
- Median : the value in the exact middle of the data set when ordered from low to high.
- Mean : the sum of all values divided by the number of values.
However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.
Calculate measures of variability
Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:
- Range : the highest value minus the lowest value of the data set.
- Interquartile range : the range of the middle half of the data set.
- Standard deviation : the average distance between each value in your data set and the mean.
- Variance : the square of the standard deviation.
Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.
Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.
From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.
It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.
A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.
Researchers often use two main methods (simultaneously) to make inferences in statistics.
- Estimation: calculating population parameters based on sample statistics.
- Hypothesis testing: a formal process for testing research predictions about the population using samples.
You can make two types of estimates of population parameters from sample statistics:
- A point estimate : a value that represents your best guess of the exact parameter.
- An interval estimate : a range of values that represent your best guess of where the parameter lies.
If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.
You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).
There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.
A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.
Hypothesis testing
Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.
Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:
- A test statistic tells you how much your data differs from the null hypothesis of the test.
- A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.
Statistical tests come in three main varieties:
- Comparison tests assess group differences in outcomes.
- Regression tests assess cause-and-effect relationships between variables.
- Correlation tests assess relationships between variables without assuming causation.
Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.
Parametric tests
Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.
A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).
- A simple linear regression includes one predictor variable and one outcome variable.
- A multiple linear regression includes two or more predictor variables and one outcome variable.
Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.
- A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
- A z test is for exactly 1 or 2 groups when the sample is large.
- An ANOVA is for 3 or more groups.
The z and t tests have subtypes based on the number and types of samples and the hypotheses:
- If you have only one sample that you want to compare to a population mean, use a one-sample test .
- If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
- If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
- If you expect a difference between groups in a specific direction, use a one-tailed test .
- If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .
The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.
However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.
You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:
- a t value (test statistic) of 3.00
- a p value of 0.0028
Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.
A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:
- a t value of 3.08
- a p value of 0.001
The final step of statistical analysis is interpreting your results.
Statistical significance
In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.
Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.
This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.
Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.
Effect size
A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.
In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .
With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.
Decision errors
Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.
You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.
Frequentist versus Bayesian statistics
Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.
However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.
Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.
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Journal of Statistics and Data Science Education
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Statistical Literacy—Misuse of Statistics and Its Consequences
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- https://doi.org/10.1080/10691898.2020.1860727
1 Introduction
2 basic organization of the seminar, 3 topics and preparation of the term paper, 4 guidelines for the preparation of the review report, 6 practical guidance for supervision, 7 feedback from the students and reflection by the authors, 8 conclusions.
- Acknowledgements
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Abstract Formulae display: ? Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Although statistical literacy has become a key competence in today’s data-driven society, it is usually not a part of statistics education. To address this issue, we propose an innovative concept for a conference-like seminar on the topic of statistical literacy. This seminar draws attention to the relevance and importance of statistical literacy, and moreover, students are made aware of the process of science communication and are introduced to the peer review process for the assessment of scientific papers. In the summer term 2020, the seminar was conducted as a joint project by the University of Hamburg, the University of Muenster, and the Joachim Herz Foundation. In this article, we present the concept of the seminar and our experience with this concept in the summer term 2020.
- Big data literacy
- Peer review
- Press release
- Statistics education
- Statistics teaching
Statistical literacy is paramount across all walks of life in our data-driven society (see, e.g., Gal Citation 2002 ; Giovannini Citation 2008 ; Galesic and Garcia-Retamero Citation 2010 ; Sharma Citation 2017 ). The relevance of statistical literacy in everyday life has already been stressed by numerous authors (see, e.g., Rumsey Citation 2002 ; Gal Citation 2003 ; Gundlach et al. Citation 2015 ), and its importance is in particular growing in a digital age where people are constantly confronted with statistics from a variety of competing sources (see Bauer, Gigerenzer, and Krämer Citation 2014 ; Sharma Citation 2017 ; Krämer, Schüller, and Quatember Citation 2019 ; Bergstrom and West Citation 2020 ). However, one cannot expect people to develop statistical literacy through instruction on general principles of statistics, due to limitations on skill transfer, lack of familiarity with critical questions, and inattention to dispositions that support statistically literate behavior (see, e.g., Lovett and Greenhouse Citation 2000 ; Gal Citation 2003 ). Therefore, it is of utmost importance that teachers and researchers are aware of the challenges of teaching statistical literacy (see, e.g., St. Clair and Chihara Citation 2012 ; Hahs-Vaughn et al. Citation 2017 ; Sharma Citation 2017 ). Moreover, the teaching of statistical thinking is a key recommendation of the GAISE College Report ASA Revision Committee ( Citation 2016 ). Whereas 20 years ago the main focus on reform efforts in statistics education was on basic lectures (as criticized by Chance and Rossman ( Citation 2001 ); Moore ( Citation 2001 ); Love and Hildebrand ( Citation 2002 )), the rapid growth of Advanced Placement (AP) statistics and inclusion of statistical concepts in primary and secondary mathematics curricula are indicative of considerable improvements. The growth of introductory statistics creates more opportunities to teach important classes like the proposed literacy seminar since more students have the necessary prerequisite knowledge and skills.
To pave the way for a successful inclusion of statistical literacy into statistics education, it is necessary to present concrete concepts that make the teaching of statistical literacy exciting for students and lecturers. There are various recent papers on teaching statistical literacy but there is no concrete teaching concept proposing a specific lecture or seminar on statistical literacy. For instance, Valentini, Carbonara, and De Candia ( Citation 2018 ) presented a gradual approach adopted by the Italian National Statistical Institute (Istat) to promote statistical literacy to university students. Christensen ( Citation 2019 ) discussed demands placed on statistics training in the 21st century against the backdrop of statistical (il)literacy by means of exemplary case studies. The article of Kadijevich and Stephens ( Citation 2020 ) deals with data discovery using automated analytics and is directed toward statistics educators to make them (more) aware of the global context concerning modern statistical literacy and data science. Watson and Callingham ( Citation 2020 ) are concerned that statistics and probability still receive inadequate attention in the classroom, leaving students without the statistical literacy needed to make sense of the claims being made in the current COVID-19 situation.
In addition to the current literature, we would like to present in this article an innovative concept for a conference-like seminar on statistical literacy entitled “Statistical Literacy—Misuse of Statistics and Its Consequences.” The seminar pursues several goals: The participants elaborate the contents and results of a specific scientific publication and uncover erroneous conclusions in media reports drawn from these studies. Students not only criticize media reports, but also write a “press release” about the publication under investigation in the framework of their term paper. Furthermore, students are introduced to the (double-blind) peer review process for the assessment of scientific papers by evaluating term papers of other participants with the help of guidelines and guiding questions provided by the supervisors. As a part of the final event of the seminar, participants present their topics and hold a subsequent scientific discussion.
In the summer term 2020, we conducted the seminar as a joint project by the University of Hamburg, the University of Muenster, and the Joachim Herz Foundation. Originally, the final event was intended to be held jointly in Hamburg (Germany) as a block seminar. However, due to the circumstances caused by the COVID-19 pandemic in 2020, the seminar was held digitally via Zoom.
The purpose of this article is to present the concept of the seminar and our experience with this concept in the summer term 2020. The article is structured as follows. Section 2 describes the basic organization of the seminar. In Section 3, topics and details regarding the preparation of the term paper are considered. Section 4 presents guidelines and guiding questions for the preparation of the review report. In Section 5, possibilities for the realization of the final event are discussed. In addition, in Section 6 practical guidance for supervision is given. Section 7 presents feedback from the students and reflection by the authors. Finally, in Section 8 concluding remarks are given.
2.1 Prerequisite and Target Groups
The prerequisite for participation of the seminar is an introductory course to statistics or basic knowledge in descriptive and inferential statistics. Apart from that there are no selection criteria or processes for participation in the seminar.
As for the target groups, the seminar is universally feasible and not limited to specific degree programs or addressed exclusively to students, but can be conducted cross- and extracurricularly.
Considering the seminar conducted in the summer term 2020,
2.2 Objectives
The main objectives of the seminar are as follows:
2.3 Requirements
Each student has to perform the following requirements:
2.4 Time Table
An exemplary time table of the seminar in the summer term 2020 can be seen in Table 1 .
Published online:
Table 1 time table of the seminar in the summer term 2020., 3.1 topics of the seminar.
Students have several alternatives for choosing a topic of their term paper:
Due to the worldwide pandemic situation during the summer term 2020, a special focus was placed on COVID-19 with topics like “Infection pathways of the coronavirus” and “Myths and facts about COVID-19.” The students obtained exemplary media reports in which the results of pertinent scientific studies are scrutinized or reproduced incorrectly.
In Example 3.1, we discuss one of the topics given in Table 2 in more detail:
Table 2 Exemplary topic list, partly inspired by the “Unstatistics of the Month.”
(Seminar topic “Coffee drinking prolongs life”). A study published in the “ Annals of Internal Medicine ” (see Gunter et al. Citation 2017 ) with more than 500,000 participants from 10 European countries shows that people with high coffee consumption have a significantly lower risk of dying. For men the probability of dying within the observation period of over 16 years was 12% lower than among non-coffee-drinkers, while for women it was 7%. Particularly in the case of death from cardiovascular and digestive diseases, significant differences in mortality rates were noted. However, only a correlation between coffee consumption and higher life expectancy was observed. A causal relationship showing coffee consumption as the cause of higher life expectancy was explicitly not identified.
After publication of the study, however, numerous media reports have been released claiming that the results of Gunter et al. ( Citation 2017 ) lead to the conclusion that coffee drinking prolongs life. But it could just as well be the exact opposite: people who are active in life and therefore live longer enjoy drinking coffee. Very few media explicitly point out the crucial difference between correlation and causality in this context (see also https://www.rwi-essen.de/unstatistik/49 ).
3.2 Assignment of Topics
According to their interests, students should choose three topic preferences that they would like to work on. The topics can be chosen arbitrarily from the alternatives listed in Section 3.1, but students should be strongly encouraged to search for and choose their own topic. On the one hand, the choice of an own topic increases the probability that this topic will be assigned to the student and on the other hand the effort of finding one’s own topic should be positively noted by the supervisors.
The students then indicate three topics in descending order of preference in the kick-off meeting. After the meeting, the supervisors assign and communicate the topics to the students. If possible, it should be tried to assign the students their first priority and to avoid assigning a topic more than once. The preparation time of the term paper starts with the announcement of the topic, that is, in the case of the seminar in summer term 2020 on April 2 (and ended on May 22).
3.3 Proposed Outline for the Term Paper
It is suggested that a term paper is structured according to the following outline:
3.4 Guidelines for the Preparation of the Press Release
In addition to criticism of media reports and/or statistical methods used, students should be able to write a “press release” for their own term paper. In this way, students are intended to be sensitized to the important process of science communication. The press release should contain a concise presentation of the main contents and results of the underlying publication. It should arouse the curiosity of the potential reader and make the content of the term paper particularly interesting. The press release is aimed at a broad, interested, but not statistically literate audience, and should have the length of 600–700 characters including spaces. Particular emphasis is to be placed on the simultaneous consideration of statistical correctness and interest arousing formulation.
The press release should be detached from the sections of the term paper, and should be placed between the title page and the table of contents. It is suggested that students receive all press releases in collected form before the start of the final event, to be prepared for the presentations.
A well-written press release could look as follows (based on a proposal from “Unstatistics of the Month”):
New results on lung cancer screening
The NELSON study investigated the efficacy of low-dose CT screening for the course of lung cancer disease. The study showed that of every 1000 men in the screening group, eight fewer died with the diagnosis of lung cancer than in the control group (from 32 to 24). However, more people with a different cancer diagnosis died in the screening group, and the overall mortality was also the same. Especially when a person has cancer in several organs, it is difficult to identify a specific cause of death. So there is no evidence that lung cancer screening saves lives.
This press release refers to the study of de Koning et al. ( Citation 2020 ) and reflects the actual results of the study. This is in sharp contrast to numerous press releases where it is stated that “the study of de Koning et al. ( Citation 2020 ) has proven that lung cancer screening saves lives, and therefore billions should be spent on its widespread implementation.” However, this conclusion is not supported by the results of the study, since in fact no lives were saved (see https://www.rwi-essen.de/unstatistik/100 ).
The students assess each other’s term papers within the framework of a double-blind peer review process, that is, the reviewer does not know the name of the author, and the author does not know the name of the reviewer. Before sending the term paper to the reviewer, the paper has to be thoroughly blinded so that no conclusion could be drawn about the author.
Since the seminar in the summer term 2020 was a joint project of two universities, reviewers and authors were chosen from different universities (as far as possible).
The students receive criteria-based guidelines and guiding questions (formulated by the supervisors, see Table 3 ), on whose basis they are intended to prepare their review report. The report should also meet the following requirements:
Table 3 Guidelines and guiding questions for the preparation of the review report.
The reviewer neither has to read the publication on which the term paper is based nor the references cited in the term paper, that is, the report is intended to be based exclusively on the term paper.
The preparation time of the review report starts with receipt of the term paper to be reviewed, that is, in the case of the seminar in summer term 2020, on May 22 (and ends on June 8). After receiving the final review reports, the supervisors send the reviews to the respective authors. Each author is expected to use the feedback to improve her/his seminar presentation and to learn from it for the Bachelor thesis (as far as the feedback is applicable). However, it is important to stress to students that the review report reflects the view of an individual student’s assessment and does not necessarily reflect the supervisor’s assessment of the term paper.
5 The Final Event: Presentations and Discussions
The final event includes the following parts:
Before the beginning of the final event, students receive the program of the final event, where time slots of the presentations are given. An exemplary seminar program from the summer term 2020 can be found in Table 4 . As outlined in Section 3.4, students also get all press releases in collected form to prepare for the presentations.
Table 4 Exemplary seminar program of the final event (summer term 2020).
The final event can take place as a classroom or digital event. In our opinion, a classroom event has some merits (e.g., direct face-to-face interaction, increased interaction between the participants, more room for spontaneity, motivation of the participants can be better overseen, joint excursions and activities are possible) and should be the preferred choice.
Since the seminar in the summer term 2020 was a joint project of three institutions, the final event was intended to be held as a block seminar in Hamburg. As part of this event, a tour program was also planned. However, due to the impact of the COVID-19 pandemic in 2020, the seminar was held as a digital event via Zoom.
In comparison with classroom events, digital events also have some benefits, such as less organizational effort for organizers and participants, independence regarding location, higher comfort of the participants, and (last but not least) they are climate-friendly due to lower travel needs. In the following, we give recommendations for conducting the seminar as a digital event. These recommendations are based on our experience with Zoom ( https://zoom.us ), but can also be applied to other software such as Skype ( https://www.skype.com ), etc.
The points that concern the students should be explicitly pointed out before the beginning of the final event as also (again) in the welcoming by the supervisors.
6.1 Responsibilities of the Supervisors
The supervisors are responsible for mentoring of students, organizing and holding the seminar as well as the assessment of a student’s scientific performance including term paper, press release, peer review report, presentation, and participation in the discussions. Intensive supervision is essential for this seminar, as the topics covered are both very comprehensive and challenging. In detail, the reasons for the significantly higher supervision effort of the students compared to common seminars are as follows:
Moreover, as usual, a supervisor’s support includes assistance with the entire preparation of the term paper, the slides and the moderation of the subsequent discussion. It is generally important that students not only work on their topics on their own, but that they are in contact with their supervisor, and the supervisors are involved in the progress so that they can take supportive and corrective action if necessary. It should also be noted that for many students this is their first seminar, so there is no experience in writing a term paper and giving a presentation.
6.2 Recommendations for Supervision
Before writing the term paper, students should be introduced to examples of misuse of statistics in the kick-off meeting, and these examples should be critically discussed by the supervisors. The majority of the students select topics proposed by the supervisors. With topics suggested by the students, they often come up with ideas for relevant newspaper articles, or TV or internet reports. The supervisors should then evaluate whether the work on the topic is feasible in the scheduled time frame and give inspiration for further media contents concerning the topic of interest.
After the topics are assigned to the students, they get time to study the material (like publications, media reports, etc.) provided by the supervisors. We recommend the students use a time span of two weeks for this process of reading and embedding the material into a first outline of the term paper. Afterward the student can make an appointment with their supervisor to discuss the outline as well as specific difficulties with advanced statistical techniques and/or from working with a dataset to reproduce the results of a discussed paper. Prior to the final event the student should provide an outline for the presentation in a further appointment. This appointment should also be used to discuss the review report received from another participant. Between those mandatory appointments the student could contact the supervisor for additional meetings if necessary.
6.3 Assessment of the Term Paper, the Review Report, and the Presentation
As for the assessment related to the requirements (see Section 2.3), we suggest the overall grade be made up with help of a weighted average of the grades for the term paper (including the press release) (60%), the review report (10%), and the presentation (30%). Guidelines for the evaluation of the term paper and the presentation can be found in Tables 5 and 6 , respectively. Since students get guidelines and guiding questions for the preparation of the review report (see Table 3 ), the assessment of the review report is based on the percentage of items addressed within the report.
Table 5 Guidelines for the evaluation of the term paper.
Table 6 guidelines for the evaluation of the presentation., 6.4 suggestions for adaptation to various settings.
As the organizational concept of the seminar is not limited to solely dealing with statistical literacy in a broad meaning, it could easily be adapted to any other context. Especially the press release, the peer review process, and the conference-like final event are attractive elements for (statistics) education. Thus, the teachers can either set a specific focus on statistical literacy (e.g., big data literacy, misuse of graphics) or adapt the seminar setting to a completely different topic of interest (which does not even have to be linked to statistics or econometrics).
Moreover, one can also vary the setting of the seminar. It is not essential for the realization of the seminar that the audience consists of students from different universities. One could still perform the peer review process without any restrictions, if all students and teachers are from the same university. As for smaller one-instructor seminars, there are basically two options. First, one could limit the number of students in the seminar and would omit the peer review process. Second, one could allow a higher number of students and divide them into groups to write one joint term paper per group. In the latter scenario, the peer review process can still be carried out (even with a smaller number of supervisors) if every group of students would review the term paper of another group.
Finally, the seminar is not limited to students and universities but can be conducted cross- and extracurricularly, as briefly outlined in Section 2.1. This includes participants and institutions of postsecondary (tertiary) education including various nondegree programs (certificates, diplomas) and degree programs (advanced intermediate, associate, bachelor, first professional, master, research doctorate). As a rule of thumb, we generally suggest a maximum of five participants per supervisor due to the extensive support of participants (see Section 6.1).
In this section, we present evidence of efficacy of the seminar and give reflections on the produced outcome.
7.1 Quantitative and Qualitative Feedback From the Students in the Summer Term 2020
After completion of the seminar and announcement of the grades in the summer term 2020, we conducted a quantitative and qualitative evaluation of the seminar. However, due to the circumstances caused by the COVID-19 pandemic, no systematic evaluations were conducted at the Faculty of Business Administration at the University of Hamburg, so there are no quantitative results for students from Hamburg. In the following, we give some representative feedback from the students.
The seminar received an average score of 1.3 from the students of Muenster, with 1 being the best and 5 the worst. In addition, the students indicated on a Likert scale (from “strongly disagree” = 1 to “strongly agree” = 7) their level of agreement with given statements. With the aim of increasing the statistical competence of the participants, the high level of agreement with the following statements is particularly pleasing:
An assessment with regard to the improvement of statistical literacy was not performed in the context of the seminar held in the summer term 2020. However, this could be supplemented easily by conducting a purposive test before the start of the seminar (e.g., at the kick-off meeting) and after the end of the seminar (e.g., after the final event). Both tests should then verify pre- and post-competences and could include the comprehension of simple statistical expressions, the judging of graphics, as well as the handling and criticism of the use of statistics in the media.
As for the qualitative feedback, we were primarily interested in (1) what the students found particularly positive, (2) how they evaluate the peer review process, and (3) what could be improved in the seminar.
(1) What do you find particularly positive about the seminar?
(2) How do you evaluate the peer review process during the seminar?
(3) What do you think could be improved in the seminar?
(supervisor’s response to the assessment is given in italic)
We have taken the special circumstances caused by COVID-19 into account in our supervision and the evaluation of a student’s performance.
General guidelines and templates for writing term papers and holding presentations can be found on the homepage of the Institute of Mathematics and Statistics. Footnote 1
An extension of the final event to three days will be considered in our next seminar when there are enough participants again.
In this seminar we had consciously decided against grading the discussion contributions to avoid an artificial discussion. However, we could implement the proposal in the upcoming seminar and assess the impact on the discussions.
7.2 Reflection by the Authors and Frank Assessment of What Was Gained
First of all, we would like to emphasize that we are pleasantly surprised by the great commitment and interest of the students in the entire seminar, that is, the writing of the term paper, the press release, the peer review report, and the final event. As for the favored topics, the students gave us feedback that they particularly appreciated topics close to everyday life. Among other things, such real-world relevance also led to a higher level of interest in the presentations given by the other students and subsequent discussions on the topic of interest. The appropriate weighting of the two key components of the term paper (presenting methodological aspects and uncovering of media biases) was sometimes challenging for the students. However, these difficulties were mainly counteracted by intensive supervision. On their own initiative, the students often searched extensively for additional references and media reports, performed an analysis of the entire data in the paper under consideration, and included the constructive feedback of the review report into their presentation and discussion during the final event. We are also pleased to note that the participants have realized the importance of profound scientific writing and correct handling of statistical concepts, not least by dealing with the review report. The participants accentuated that they intend to transfer the knowledge gained in the seminar directly into their bachelor’s theses. Thus, we conclude that the seminar has a considerable impact on the awareness of participants for the correct use of statistical methods.
As for a frank assessment of what was gained, in the first line, students were made aware of the frequently wrong or misleading use of statistics in scientific publications and media contributions. In the second line, they have been sensitized to deal with them and, contrary to the basic courses in statistics, have seen how statistical terms are used in everyday life. This seems to be very important to us at a time when fake news is becoming increasingly common. Especially when bearing in mind that fake news can easily be enriched by adding wrong statistics (based on misused statistical methods) giving them a scientific appearance whose validity can only be judged by statistical literate individuals.
Statistical literacy requires not only a series of basic skills (such as reading, comprehension, and communication) but also higher order cognitive skills of interpretation, prediction, and critical thinking. The ability to interpret statistics critically and to refute claims is not innate; these skills need to be taught adequately if students are to become informed individuals (see Sharma Citation 2017 ). To address this issue and to contribute to the improvement of statistics education, we have presented an innovative concept for a seminar on statistical literacy in this article. This seminar not only creates an awareness of statistical literacy among the participants, but is also versatile and exciting for participants and lecturers because of its manifold objectives including performing detective work (revealing false conclusions in media reports) as well as writing of a press release and a review report. Since the only prerequisite of participation is basic knowledge in descriptive and inferential statistics, the proposed seminar can be conducted cross- and extracurricularly.
We have successfully carried out a seminar according to this concept in the summer term 2020. It is the declared aim to establish the seminar on statistical literacy as an integral part of statistics education and to continue the joint project between the Joachim Herz Foundation, the University of Muenster, and the University of Hamburg. Accordingly, one seminar per year, each in the summer term, is planned. Moreover, the upcoming seminar will focus even more on current problems and is planned to be dedicated to big data literacy (see also Francois, Monteiro, and Allo Citation 2020 ).
Finally, with this article, we hope to have provided a concept for the direct incorporation of statistical literacy into statistics education that will set a standard. We are very interested in an exchange with and feedback from other lecturers on our seminar concept.
Acknowledgments
The authors thank Mark Trede for his extraordinary support. The authors are also grateful to the associate editor and two anonymous reviewers for their valuable feedback and suggestions, which were important and helpful to improve the article.
Ethical Guidelines
Since the seminar was not intended to be a research project but conducted as a regular course on statistical literacy, it is in line with ethical guidelines to protect human subjects. Due to the high degree of innovation of the seminar concept and its benefits for the improvement of statistics education, we decided in retrospect to present its concept in a scientific paper and to share our experience and audience feedback with educators, practitioners, and researchers around the world. In the summer term 2020, a total evaluation of the seminar at the University of Muenster as well as a qualitative evaluation and an evaluation of the peer review process at both universities were performed. Participation was voluntary in both cases.
1 See https://www.bwl.uni-hamburg.de/matstat/studium/abschlussarbeiten.html
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Home > Mathematics and Statistics > MathStat TDs > Masters Theses
Mathematics and Statistics Masters Theses
Theses from 2022 2022.
Several problems in nonlinear Schrödinger equations , Tim Van Hoose
Theses from 2020 2020
Decoupled finite element methods for general steady two-dimensional Boussinesq equations , Lioba Boveleth
Quantifying effects of sleep deprivation on cognitive performance , Quang Nghia Le
The application of machine learning models in the concussion diagnosis process , Sujit Subhash
Theses from 2019 2019
Less is more: Beating the market with recurrent reinforcement learning , Louis Kurt Bernhard Steinmeister
Theses from 2018 2018
Models for high dimensional spatially correlated risks and application to thunderstorm loss data in Texas , Tobias Merk
An investigation of the influence of the 2007-2009 recession on the day of the week effect for the S&P 500 and its sectors , Marcel Alwin Trick
Theses from 2017 2017
The pantograph equation in quantum calculus , Thomas Griebel
Comparing region level testing methods for differential DNA methylation analysis , Arnold Albert Harder
A review of random matrix theory with an application to biological data , Jesse Aaron Marks
Family-based association studies of autism in boys via facial-feature clusters , Luke Andrew Settles
Theses from 2016 2016
Pricing of geometric Asian options in general affine stochastic volatility models , Johannes Ruppert
On the double chain ladder for reserve estimation with bootstrap applications , Larissa Schoepf
Theses from 2015 2015
Some combinatorial applications of Sage, an open source program , Jessica Ruth Chowning
Day of the week effect in returns and volatility of the S&P 500 sector indices , Juan Liu
Application of loglinear models to claims triangle runoff data , Netanya Lee Martin
Theses from 2014 2014
Adaptive wavelet discretization of tensor products in H-Tucker format , Mazen Ali
An iterative algorithm for variational data assimilation problems , Xin Shen
Statistical analysis of sleep patterns in Drosophila melanogaster , Luyang Wang
Theses from 2013 2013
Statistical analysis of microarray data in sleep deprivation , Stephanie Marie Berhorst
Immersed finite element method for interface problems with algebraic multigrid solver , Wenqiang Feng
Theses from 2012 2012
Abel dynamic equations of the first and second kind , Sabrina Heike Streipert
Lattice residuability , Philip Theodore Thiem
Theses from 2011 2011
A time series approach to electric load modelling , Matthias Benjamin Noller
Theses from 2010 2010
Closed-form solutions to discrete-time portfolio optimization problems , Mathias Christian Goeggel
Inverse limits with upper semi-continuous set valued bonding functions: an example , Christopher David Jacobsen
Theses from 2009 2009
The analogue of the iterated logarithm for quantum difference equations , Karl Friedrich Ulrich
Theses from 2008 2008
Modeling particulate matter emissions indices at the Hartsfield-Jackson Atlanta International Airport , Lu Gan
The dynamic multiplier-accelerator model in economics , Julius Severi Heim
Dynamic equations with piecewise continuous argument , Christian Keller
Theses from 2007 2007
Ostrowski and Grüss inequalities on time scales , Thomas Matthews
The Black-Scholes equation in quantum calculus , Christian Müttel
Computerized proofs of hypergeometric identities: Methods, advances, and limitations , Paul Nathaniel Runnion
Screening for noise variables , Lisa Trautwein
Theses from 2006 2006
Distance function applications of object comparison in artificial vision systems , Christina Michelle Ayres
Sensitivity analysis on the relationship between alcohol abuse or dependence and wages , Tim Jensen
Sensitivity analysis on the relationship between alcohol abuse or dependence and annual hours worked , Stefan Koerner
Endogeneity bias and two-stage least squares: a simulation study , Xujun Wang
Theses from 2005 2005
Local compactness of the hyperspace of connected subsets , Robbie A. Beane
A sequential approach to supersaturated design , Angela Marie Jugan
Tests for gene-treatment interaction in microarray data analysis , Wanrong Yin
Theses from 2003 2003
Pricing of European options , Dirk Rohmeder
Prediction intervals for the binomial distribution with dependent trials , Florian Sebastian Rueck
Theses from 2002 2002
The use of a Marakov dependent Bernoulli process to model the relationship between employment status and drug use , Kathrin Koetting
Theses from 2000 2000
Inverse limits on [0,1] using sequences of piecewise linear unimodal bonding maps , Brian Edward Raines
Theses from 1998 1998
A two-stage step-stress accelerated life testing scheme , Phyllis E. Pound Singer
Theses from 1997 1997
Some properties of hereditarily indecomposable chainable continua , Thomas John Kacvinsky
Theses from 1996 1996
The Axiom of Choice, well-ordering property, Continuum Hypothesis, and other meta-mathematical considerations , Daniel Collins
Theses from 1994 1994
Approximate distributional results for tolerance limits and confidence limits on reliability based on the maximum likelihood estimators for the logistic distribution , Teriann Collins
Theses from 1986 1986
Investigating the output angular acceleration extrema of the planar four bar mechanism , Matthew H. Koebbe
Theses from 1984 1984
Approximating distributions in order restricted inference : the simple tree ordering , Tuan Anh Tran
Theses from 1982 1982
Goodness-of-fit for the Weibull distribution with unknown parameters and censored sampling. , Michael Edward Aho
Theses from 1979 1979
On L convergence of Fourier series. , William O. Bray
Theses from 1977 1977
Characterizations of inner product spaces. , John Lee Roy Williams
Theses from 1975 1975
A study of several substitution ciphers using mathematical models. , Wanda Louise Garner
Theses from 1974 1974
Models for molecular vibration , Allan Bruce Capps
The completions of local rings and their modules. , Christopher Scott Taber
Linear geometry , Phyllis L. Thomas
Theses from 1971 1971
Integrability of the sums of the trigonometric series 1/2 aₒ + ∞ [over] Σ [over] n=1 a n cos nΘ and ∞ [over] Σ [over] n=1 a n sin nΘ , John William Garrett
Inclusion theorems for boundary value problems for delay differential equations , Leon M. Hall
Theses from 1965 1965
A study of certain conservative sets for parameters in the linear statistical model , Roger Alan Chapin
Comparison of methods to select a probability model , Howard Lyndal Colburn
Latent class analysis and information retrieval , George Loyd Jensen
Linear and quadratic programming with more than one objective function , William John Lodholz
Tschebyscheff fitting with polynomials and nonlinear functions , George F. Luffel
Theses from 1964 1964
The effect of matrix condition in the solution of a system of linear algebraic equations. , Herbert R. Alcorn
Estimation and tabulation of bias coefficients for regression analysis in incompletely specified linear models. , Harry Kerry Edwards
A study of a method for selecting the best of two or more mathematical models , August J. Garver
A study of methods for estimating parameters in the model y(t) = A₁e -p₁t + A₂e -p₂t + ϵ , Gerald Nicholas Haas
A parameter perturbation procedure for obtaining a solution to systems of nonlinear equations. , James Carlton Helm
A study of stability of numerical solution for parabolic partial differential equations. , Tsang-Chi Huang
A numerical study of Van Der Pol's nonlinear differential equation for various values of the parameter E. , Charles C. Limbaugh
A study on estimating parameters restricted by linear inequalities , William Lawrence May
Minimization of Boolean functions. , Don Laroy Rogier
A method to give the best linear combination of order statistics to estimate the mean of any symmetric population , Robert M. Smith
On a numerical solution of Dirichlet type problems with singularity on the boundary. , Randall Loran Yoakum
Theses from 1963 1963
A study of methods for estimating parameters in rational polynomial models , Thomas B. Baird
Investigation of measures of ill-conditioning , Thomas D. Calton
A numerical approach to a Sturm-Liouville type problem with variable coefficients and its application to heat transfer and temperature prediction in the lower atmosphere. , Troyce Don Jones
A study of methods for determining confidence intervals for the mean of a normal distribution with unknown varience by comparison of average lengths , Karl Richard Kneile
Stability properties of various predictor corrector methods for solving ordinary differential equations numerically. , Charles Edward. Leslie
Mathematical techniques in the solution of boundary value problems. , Vincent Paul Pusateri
A modified algorithm for Henrici's solution of y' ' = f (x,y) , Frank Garnett Walters
Theses from 1962 1962
An investigation of Lehmer's method for finding the roots of polynomial equations using the Royal-McBee LGP-30 , James W. Joiner
Theses from 1931 1931
The spinning top , Aaron Jefferson Miles
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Thesis on Statistics
Statistics thesis is a complex document which requires interpretation, analysis and knowledge of all aspects of data. The task requires a lot of quality and authentic research material. Researchomatic has therefore dedicated an entire section to statistics thesis, which will help students to gain useful ideas about their own work. From the initial collection to the final data representation, these theses have been developed with stringent care.
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The Use Of Classification Trees (Data Mining) In Predictive Ecology
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An thesis examples on statistics statement is a prosaic composition of a small volume and free composition, expressing individual impressions and thoughts on a specific occasion or issue and obviously not claiming a definitive or exhaustive interpretation of the subject.
Some signs of statistics statement thesis:
- the presence of a specific topic or question. A work devoted to the analysis of a wide range of problems in biology, by definition, cannot be performed in the genre of statistics statement thesis topic.
- The thesis expresses individual impressions and thoughts on a specific occasion or issue, in this case, on statistics statement and does not knowingly pretend to a definitive or exhaustive interpretation of the subject.
- As a rule, an essay suggests a new, subjectively colored word about something, such a work may have a philosophical, historical, biographical, journalistic, literary, critical, popular scientific or purely fiction character.
- in the content of an thesis samples on statistics statement, first of all, the author’s personality is assessed - his worldview, thoughts and feelings.
The goal of an thesis in statistics statement is to develop such skills as independent creative thinking and writing out your own thoughts.
Writing an thesis is extremely useful, because it allows the author to learn to clearly and correctly formulate thoughts, structure information, use basic concepts, highlight causal relationships, illustrate experience with relevant examples, and substantiate his conclusions.
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- Master's Thesis
As part of the Completion Exercise for the Master's of Science in Statistical Science degree, you may write and present your Master's Thesis . This oral examination is administered by your Master's Committee. Students choosing to defend a thesis should begin work on their research as early as possible, preferably in their second semester or summer after their first year in the program. Please give yourself enough time to write your thesis. Your thesis advisor (chair of your committee) should approve your thesis title. The work has to be approved by all members of your committee.
Master’s BEST Award: Each 2 nd year Duke Master’s of Statistical Science (MSS) student defending their MSS thesis may be eligible for the Master’s BEST Award . The selection of the awardee is made by the Statistical Science faculty BEST Award Committee on the basis of the submitted thesis of MSS thesis students in the same year.
All students choosing to do a thesis should submit a thesis proposal (not more than two pages) to the MS Director via Qualtrics by October 15th (of your third semester). The thesis proposal should include a title (it can be tentative and refined later), a list of three committee members (two should be from the Statistics Department, including the chair), and a description of your work.
Please note: The Master's Thesis Committee should be formed and approved by The Graduate School at least 30 days prior to your thesis defense.
For details, see the document below.
The Thesis consists of a detailed written report on a project approved by the M.S. Director and the student's thesis advisor, covering aspects of your contribution to the project area :
- introduction
- summary of contributions and results
- discussion of open questions
- bibliographic material
The Master's Thesis and its submission must conform to the Duke University Graduate School M.S. thesis requirements . All students choosing to do Master's Thesis should follow the steps outlined in the MSS Thesis Defense Process document.
- Hierarchical Signal Propagation for Household Level Sales in Bayesian Dynamic Models
- Logistic Tree Gaussian Processes (LoTgGaP) for Microbiome Dynamics and Treatment Effects
- Bayesian Inference on Ratios Subject to Differentially Private Noise
- Multiple Imputation Inferences for Count Data
- An Euler Characteristic Curve Based Representation of 3D Shapes in Statistical Analysis
- An Investigation Into the Bias & Variance of Almost Matching Exactly Methods
- Comparison of Bayesian Inference Methods for Probit Network Models
- Differentially Private Counts with Additive Constraints
- Multi-Scale Graph Principal Component Analysis for Connectomics
- MCMC Sampling Geospatial Partitions for Linear Models
- Bayesian Dynamic Network Modeling with Censored Flow Data
- An Application of Graph Diffusion for Gesture Classification
- Easy and Efficient Bayesian Infinite Factor Analysis
- Analyzing Amazon CD Reviews with Bayesian Monitoring and Machine Learning Methods
- Missing Data Imputation for Voter Turnout Using Auxiliary Margins
- Generalized and Scalable Optimal Sparse Decision Trees
- Construction of Objective Bayesian Prior from Bertrand’s Paradox and the Principle of Indifference
- Rethinking Non-Linear Instrumental Variables
- Clustering-Enhanced Stochastic Gradient MCMC for Hidden Markov Models
- Optimal Sparse Decision Trees
- Bayesian Density Regression with a Jump Discontinuity at a Given Threshold
- Forecasting the Term Structure of Interest Rates: A Bayesian Dynamic Graphical Modeling Approach
- Testing Between Different Types of Poisson Mixtures with Applications to Neuroscience
- Multiple Imputation of Missing Covariates in Randomized Controlled Trials
- A Bayesian Strategy to the 20 Question Game with Applications to Recommender Systems
- Applied Factor Dynamic Analysis for Macroeconomic Forecasting
- A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results
- Bayesian Inference Via Partitioning Under Differential Privacy
- A Bayesian Forward Simulation Approach to Establishing a Realistic Prior Model for Complex Geometrical Objects
- Two Applications of Summary Statistics: Integrating Information Across Genes and Confidence Intervals with Missing Data
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Purdue Online Writing Lab College of Liberal Arts
Writing with Descriptive Statistics
Welcome to the Purdue OWL
This page is brought to you by the OWL at Purdue University. When printing this page, you must include the entire legal notice.
Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved. This material may not be published, reproduced, broadcast, rewritten, or redistributed without permission. Use of this site constitutes acceptance of our terms and conditions of fair use.
This handout explains how to write with statistics including quick tips, writing descriptive statistics, writing inferential statistics, and using visuals with statistics.
Usually there is no good way to write a statistic. It rarely sounds good, and often interrupts the structure or flow of your writing. Oftentimes the best way to write descriptive statistics is to be direct. If you are citing several statistics about the same topic, it may be best to include them all in the same paragraph or section.
The mean of exam two is 77.7. The median is 75, and the mode is 79. Exam two had a standard deviation of 11.6.
Overall the company had another excellent year. We shipped 14.3 tons of fertilizer for the year, and averaged 1.7 tons of fertilizer during the summer months. This is an increase over last year, where we shipped only 13.1 tons of fertilizer, and averaged only 1.4 tons during the summer months. (Standard deviations were as followed: this summer .3 tons, last summer .4 tons).
Some fields prefer to put means and standard deviations in parentheses like this:
If you have lots of statistics to report, you should strongly consider presenting them in tables or some other visual form. You would then highlight statistics of interest in your text, but would not report all of the statistics. See the section on statistics and visuals for more details.
If you have a data set that you are using (such as all the scores from an exam) it would be unusual to include all of the scores in a paper or article. One of the reasons to use statistics is to condense large amounts of information into more manageable chunks; presenting your entire data set defeats this purpose.
At the bare minimum, if you are presenting statistics on a data set, it should include the mean and probably the standard deviation. This is the minimum information needed to get an idea of what the distribution of your data set might look like. How much additional information you include is entirely up to you. In general, don't include information if it is irrelevant to your argument or purpose. If you include statistics that many of your readers would not understand, consider adding the statistics in a footnote or appendix that explains it in more detail.
Top 99+ Trending Statistics Research Topics for Students
Being a statistics student, finding the best statistics research topics is quite challenging. But not anymore; find the best statistics research topics now!!!
Statistics is one of the tough subjects because it consists of lots of formulas, equations and many more. Therefore the students need to spend their time to understand these concepts. And when it comes to finding the best statistics research project for their topics, statistics students are always looking for someone to help them.
In this blog, we will share with you the most interesting and trending statistics research topics in 2023. It will not just help you to stand out in your class but also help you to explore more about the world.
As you know, it is always suggested that you should work on interesting topics. That is why we have mentioned the most interesting research topics for college students and high school students. Here in this blog post, we will share with you the list of 99+ awesome statistics research topics.
Why Do We Need to Have Good Statistics Research Topics?
Table of Contents
Having a good research topic will not just help you score good grades, but it will also allow you to finish your project quickly. Because whenever we work on something interesting, our productivity automatically boosts. Thus, you need not invest lots of time and effort, and you can achieve the best with minimal effort and time.
What Are Some Interesting Research Topics?
If we talk about the interesting research topics in statistics, it can vary from student to student. But here are the key topics that are quite interesting for almost every student:-
- Literacy rate in a city.
- Abortion and pregnancy rate in the USA.
- Eating disorders in the citizens.
- Parent role in self-esteem and confidence of the student.
- Uses of AI in our daily life to business corporates.
Top 99+ Trending Statistics Research Topics For 2023
Here in this section, we will tell you more than 99 trending statistics research topics:
Sports Statistics Research Topics
- Statistical analysis for legs and head injuries in Football.
- Statistical analysis for shoulder and knee injuries in MotoGP.
- Deep statistical evaluation for the doping test in sports from the past decade.
- Statistical observation on the performance of athletes in the last Olympics.
- Role and effect of sports in the life of the student.
Psychology Research Topics for Statistics
- Deep statistical analysis of the effect of obesity on the student’s mental health in high school and college students.
- Statistical evolution to find out the suicide reason among students and adults.
- Statistics analysis to find out the effect of divorce on children in a country.
- Psychology affects women because of the gender gap in specific country areas.
- Statistics analysis to find out the cause of online bullying in students’ lives.
- In Psychology, PTSD and descriptive tendencies are discussed.
- The function of researchers in statistical testing and probability.
- Acceptable significance and probability thresholds in clinical Psychology.
- The utilization of hypothesis and the role of P 0.05 for improved comprehension.
- What types of statistical data are typically rejected in psychology?
- The application of basic statistical principles and reasoning in psychological analysis.
- The role of correlation is when several psychological concepts are at risk.
- Actual case study learning and modeling are used to generate statistical reports.
- In psychology, naturalistic observation is used as a research sample.
- How should descriptive statistics be used to represent behavioral data sets?
Applied Statistics Research Topics
- Does education have a deep impact on the financial success of an individual?
- The investment in digital technology is having a meaningful return for corporations?
- The gap of financial wealth between rich and poor in the USA.
- A statistical approach to identify the effects of high-frequency trading in financial markets.
- Statistics analysis to determine the impact of the multi-agent model in financial markets.
Personalized Medicine Statistics Research Topics
- Statistical analysis on the effect of methamphetamine on substance abusers.
- Deep research on the impact of the Corona vaccine on the Omnicrone variant.
- Find out the best cancer treatment approach between orthodox therapies and alternative therapies.
- Statistics analysis to identify the role of genes in the child’s overall immunity.
- What factors help the patients to survive from Coronavirus .
Experimental Design Statistics Research Topics
- Generic vs private education is one of the best for the students and has better financial return.
- Psychology vs physiology: which leads the person not to quit their addictions?
- Effect of breastmilk vs packed milk on the infant child overall development
- Which causes more accidents: male alcoholics vs female alcoholics.
- What causes the student not to reveal the cyberbullying in front of their parents in most cases.
Easy Statistics Research Topics
- Application of statistics in the world of data science
- Statistics for finance: how statistics is helping the company to grow their finance
- Advantages and disadvantages of Radar chart
- Minor marriages in south-east Asia and African countries.
- Discussion of ANOVA and correlation.
- What statistical methods are most effective for active sports?
- When measuring the correctness of college tests, a ranking statistical approach is used.
- Statistics play an important role in Data Mining operations.
- The practical application of heat estimation in engineering fields.
- In the field of speech recognition, statistical analysis is used.
- Estimating probiotics: how much time is necessary for an accurate statistical sample?
- How will the United States population grow in the next twenty years?
- The legislation and statistical reports deal with contentious issues.
- The application of empirical entropy approaches with online grammar checking.
- Transparency in statistical methodology and the reporting system of the United States Census Bureau.
Statistical Research Topics for High School
- Uses of statistics in chemometrics
- Statistics in business analytics and business intelligence
- Importance of statistics in physics.
- Deep discussion about multivariate statistics
- Uses of Statistics in machine learning
Survey Topics for Statistics
- Gather the data of the most qualified professionals in a specific area.
- Survey the time wasted by the students in watching Tvs or Netflix.
- Have a survey the fully vaccinated people in the USA
- Gather information on the effect of a government survey on the life of citizens
- Survey to identify the English speakers in the world.
Statistics Research Paper Topics for Graduates
- Have a deep decision of Bayes theorems
- Discuss the Bayesian hierarchical models
- Analysis of the process of Japanese restaurants.
- Deep analysis of Lévy’s continuity theorem
- Analysis of the principle of maximum entropy
AP Statistics Topics
- Discuss about the importance of econometrics
- Analyze the pros and cons of Probit Model
- Types of probability models and their uses
- Deep discussion of ortho stochastic matrix
- Find out the ways to get an adjacency matrix quickly
Good Statistics Research Topics
- National income and the regulation of cryptocurrency.
- The benefits and drawbacks of regression analysis.
- How can estimate methods be used to correct statistical differences?
- Mathematical prediction models vs observation tactics.
- In sociology research, there is bias in quantitative data analysis.
- Inferential analytical approaches vs. descriptive statistics.
- How reliable are AI-based methods in statistical analysis?
- The internet news reporting and the fluctuations: statistics reports.
- The importance of estimate in modeled statistics and artificial sampling.
Business Statistics Topics
- Role of statistics in business in 2023
- Importance of business statistics and analytics
- What is the role of central tendency and dispersion in statistics
- Best process of sampling business data.
- Importance of statistics in big data.
- The characteristics of business data sampling: benefits and cons of software solutions.
- How may two different business tasks be tackled concurrently using linear regression analysis?
- In economic data relations, index numbers, random probability, and correctness are all important.
- The advantages of a dataset approach to statistics in programming statistics.
- Commercial statistics: how should the data be prepared for maximum accuracy?
Statistical Research Topics for College Students
- Evaluate the role of John Tukey’s contribution to statistics.
- The role of statistics to improve ADHD treatment.
- The uses and timeline of probability in statistics.
- Deep analysis of Gertrude Cox’s experimental design in statistics.
- Discuss about Florence Nightingale in statistics.
- What sorts of music do college students prefer?
- The Main Effect of Different Subjects on Student Performance.
- The Importance of Analytics in Statistics Research.
- The Influence of a Better Student in Class.
- Do extracurricular activities help in the transformation of personalities?
- Backbenchers’ Impact on Class Performance.
- Medication’s Importance in Class Performance.
- Are e-books better than traditional books?
- Choosing aspects of a subject in college
How To Write Good Statistics Research Topics?
So, the main question that arises here is how you can write good statistics research topics. The trick is understanding the methodology that is used to collect and interpret statistical data. However, if you are trying to pick any topic for your statistics project, you must think about it before going any further.
As a result, it will teach you about the data types that will be researched because the sample will be chosen correctly. On the other hand, your basic outline for choosing the correct topics is as follows:
- Introduction of a problem
- Methodology explanation and choice.
- Statistical research itself is in the main part (Body Part).
- Samples deviations and variables.
- Lastly, statistical interpretation is your last part (conclusion).
Note: Always include the sources from which you obtained the statistics data.
Top 3 Tips to Choose Good Statistics Research Topics
It can be quite easy for some students to pick a good statistics research topic without the help of an essay writer . But we know that it is not a common scenario for every student. That is why we will mention some of the best tips that will help you choose good statistics research topics for your next project. Either you are in a hurry or have enough time to explore. These tips will help you in every scenario.
1. Narrow down your research topic
We all start with many topics as we are not sure about our specific interests or niche. The initial step to picking up a good research topic for college or school students is to narrow down the research topic.
For this, you need to categorize the matter first. And then pick a specific category as per your interest. After that, brainstorm about the topic’s content and how you can make the points catchy, focused, directional, clear, and specific.
2. Choose a topic that gives you curiosity
After categorizing the statistics research topics, it is time to pick one from the category. Don’t pick the most common topic because it will not help your grades and knowledge. Instead of it, please choose the best one, in which you have little information, or you are more likely to explore it.
In a statistics research paper, you always can explore something beyond your studies. By doing this, you will be more energetic to work on this project. And you will also feel glad to get them lots of information you were willing to have but didn’t get because of any reasons.
It will also make your professor happy to see your work. Ultimately it will affect your grades with a positive attitude.
3. Choose a manageable topic
Now you have decided on the topic, but you need to make sure that your research topic should be manageable. You will have limited time and resources to complete your project if you pick one of the deep statistics research topics with massive information.
Then you will struggle at the last moment and most probably not going to finish your project on time. Therefore, spend enough time exploring the topic and have a good idea about the time duration and resources you will use for the project.
Statistics research topics are massive in numbers. Because statistics operations can be performed on anything from our psychology to our fitness. Therefore there are lots more statistics research topics to explore. But if you are not finding it challenging, then you can take the help of our statistics experts . They will help you to pick the most interesting and trending statistics research topics for your projects.
With this help, you can also save your precious time to invest it in something else. You can also come up with a plethora of topics of your choice and we will help you to pick the best one among them. Apart from that, if you are working on a project and you are not sure whether that is the topic that excites you to work on it or not. Then we can also help you to clear all your doubts on the statistics research topic.
Frequently Asked Questions
Q1. what are some good topics for the statistics project.
Have a look at some good topics for statistics projects:- 1. Research the average height and physics of basketball players. 2. Birth and death rate in a specific city or country. 3. Study on the obesity rate of children and adults in the USA. 4. The growth rate of China in the past few years 5. Major causes of injury in Football
Q2. What are the topics in statistics?
Statistics has lots of topics. It is hard to cover all of them in a short answer. But here are the major ones: conditional probability, variance, random variable, probability distributions, common discrete, and many more.
Q3. What are the top 10 research topics?
Here are the top 10 research topics that you can try in 2023:
1. Plant Science 2. Mental health 3. Nutritional Immunology 4. Mood disorders 5. Aging brains 6. Infectious disease 7. Music therapy 8. Political misinformation 9. Canine Connection 10. Sustainable agriculture
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A Grand Journey of Statistical Hierarchical Modelling
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There are lies, damned lies, and statistics. —Mark Twain
What this handout is about
The purpose of this handout is to help you use statistics to make your argument as effectively as possible.
Introduction
Numbers are power. Apparently freed of all the squishiness and ambiguity of words, numbers and statistics are powerful pieces of evidence that can effectively strengthen any argument. But statistics are not a panacea. As simple and straightforward as these little numbers promise to be, statistics, if not used carefully, can create more problems than they solve.
Many writers lack a firm grasp of the statistics they are using. The average reader does not know how to properly evaluate and interpret the statistics he or she reads. The main reason behind the poor use of statistics is a lack of understanding about what statistics can and cannot do. Many people think that statistics can speak for themselves. But numbers are as ambiguous as words and need just as much explanation.
In many ways, this problem is quite similar to that experienced with direct quotes. Too often, quotes are expected to do all the work and are treated as part of the argument, rather than a piece of evidence requiring interpretation (see our handout on how to quote .) But if you leave the interpretation up to the reader, who knows what sort of off-the-wall interpretations may result? The only way to avoid this danger is to supply the interpretation yourself.
But before we start writing statistics, let’s actually read a few.
Reading statistics
As stated before, numbers are powerful. This is one of the reasons why statistics can be such persuasive pieces of evidence. However, this same power can also make numbers and statistics intimidating. That is, we too often accept them as gospel, without ever questioning their veracity or appropriateness. While this may seem like a positive trait when you plug them into your paper and pray for your reader to submit to their power, remember that before we are writers of statistics, we are readers. And to be effective readers means asking the hard questions. Below you will find a useful set of hard questions to ask of the numbers you find.
1. Does your evidence come from reliable sources?
This is an important question not only with statistics, but with any evidence you use in your papers. As we will see in this handout, there are many ways statistics can be played with and misrepresented in order to produce a desired outcome. Therefore, you want to take your statistics from reliable sources (for more information on finding reliable sources, please see our handout on evaluating print sources ). This is not to say that reliable sources are infallible, but only that they are probably less likely to use deceptive practices. With a credible source, you may not need to worry as much about the questions that follow. Still, remember that reading statistics is a bit like being in the middle of a war: trust no one; suspect everyone.
2. What is the data’s background?
Data and statistics do not just fall from heaven fully formed. They are always the product of research. Therefore, to understand the statistics, you should also know where they come from. For example, if the statistics come from a survey or poll, some questions to ask include:
- Who asked the questions in the survey/poll?
- What, exactly, were the questions?
- Who interpreted the data?
- What issue prompted the survey/poll?
- What (policy/procedure) potentially hinges on the results of the poll?
- Who stands to gain from particular interpretations of the data?
All these questions help you orient yourself toward possible biases or weaknesses in the data you are reading. The goal of this exercise is not to find “pure, objective” data but to make any biases explicit, in order to more accurately interpret the evidence.
3. Are all data reported?
In most cases, the answer to this question is easy: no, they aren’t. Therefore, a better way to think about this issue is to ask whether all data have been presented in context. But it is much more complicated when you consider the bigger issue, which is whether the text or source presents enough evidence for you to draw your own conclusion. A reliable source should not exclude data that contradicts or weakens the information presented.
An example can be found on the evening news. If you think about ice storms, which make life so difficult in the winter, you will certainly remember the newscasters warning people to stay off the roads because they are so treacherous. To verify this point, they tell you that the Highway Patrol has already reported 25 accidents during the day. Their intention is to scare you into staying home with this number. While this number sounds high, some studies have found that the number of accidents actually goes down on days with severe weather. Why is that? One possible explanation is that with fewer people on the road, even with the dangerous conditions, the number of accidents will be less than on an “average” day. The critical lesson here is that even when the general interpretation is “accurate,” the data may not actually be evidence for the particular interpretation. This means you have no way to verify if the interpretation is in fact correct.
There is generally a comparison implied in the use of statistics. How can you make a valid comparison without having all the facts? Good question. You may have to look to another source or sources to find all the data you need.
4. Have the data been interpreted correctly?
If the author gives you her statistics, it is always wise to interpret them yourself. That is, while it is useful to read and understand the author’s interpretation, it is merely that—an interpretation. It is not the final word on the matter. Furthermore, sometimes authors (including you, so be careful) can use perfectly good statistics and come up with perfectly bad interpretations. Here are two common mistakes to watch out for:
- Confusing correlation with causation. Just because two things vary together does not mean that one of them is causing the other. It could be nothing more than a coincidence, or both could be caused by a third factor. Such a relationship is called spurious.The classic example is a study that found that the more firefighters sent to put out a fire, the more damage the fire did. Yikes! I thought firefighters were supposed to make things better, not worse! But before we start shutting down fire stations, it might be useful to entertain alternative explanations. This seemingly contradictory finding can be easily explained by pointing to a third factor that causes both: the size of the fire. The lesson here? Correlation does not equal causation. So it is important not only to think about showing that two variables co-vary, but also about the causal mechanism.
- Ignoring the margin of error. When survey results are reported, they frequently include a margin of error. You might see this written as “a margin of error of plus or minus 5 percentage points.” What does this mean? The simple story is that surveys are normally generated from samples of a larger population, and thus they are never exact. There is always a confidence interval within which the general population is expected to fall. Thus, if I say that the number of UNC students who find it difficult to use statistics in their writing is 60%, plus or minus 4%, that means, assuming the normal confidence interval of 95%, that with 95% certainty we can say that the actual number is between 56% and 64%.
Why does this matter? Because if after introducing this handout to the students of UNC, a new poll finds that only 56%, plus or minus 3%, are having difficulty with statistics, I could go to the Writing Center director and ask for a raise, since I have made a significant contribution to the writing skills of the students on campus. However, she would no doubt point out that a) this may be a spurious relationship (see above) and b) the actual change is not significant because it falls within the margin of error for the original results. The lesson here? Margins of error matter, so you cannot just compare simple percentages.
Finally, you should keep in mind that the source you are actually looking at may not be the original source of your data. That is, if you find an essay that quotes a number of statistics in support of its argument, often the author of the essay is using someone else’s data. Thus, you need to consider not only your source, but the author’s sources as well.
Writing statistics
As you write with statistics, remember your own experience as a reader of statistics. Don’t forget how frustrated you were when you came across unclear statistics and how thankful you were to read well-presented ones. It is a sign of respect to your reader to be as clear and straightforward as you can be with your numbers. Nobody likes to be played for a fool. Thus, even if you think that changing the numbers just a little bit will help your argument, do not give in to the temptation.
As you begin writing, keep the following in mind. First, your reader will want to know the answers to the same questions that we discussed above. Second, you want to present your statistics in a clear, unambiguous manner. Below you will find a list of some common pitfalls in the world of statistics, along with suggestions for avoiding them.
1. The mistake of the “average” writer
Nobody wants to be average. Moreover, nobody wants to just see the word “average” in a piece of writing. Why? Because nobody knows exactly what it means. There are not one, not two, but three different definitions of “average” in statistics, and when you use the word, your reader has only a 33.3% chance of guessing correctly which one you mean.
For the following definitions, please refer to this set of numbers: 5, 5, 5, 8, 12, 14, 21, 33, 38
- Mean (arithmetic mean) This may be the most average definition of average (whatever that means). This is the weighted average—a total of all numbers included divided by the quantity of numbers represented. Thus the mean of the above set of numbers is 5+5+5+8+12+14+21+33+38, all divided by 9, which equals 15.644444444444 (Wow! That is a lot of numbers after the decimal—what do we do about that? Precision is a good thing, but too much of it is over the top; it does not necessarily make your argument any stronger. Consider the reasonable amount of precision based on your input and round accordingly. In this case, 15.6 should do the trick.)
- Median Depending on whether you have an odd or even set of numbers, the median is either a) the number midway through an odd set of numbers or b) a value halfway between the two middle numbers in an even set. For the above set (an odd set of 9 numbers), the median is 12. (5, 5, 5, 8 < 12 < 14, 21, 33, 38)
- Mode The mode is the number or value that occurs most frequently in a series. If, by some cruel twist of fate, two or more values occur with the same frequency, then you take the mean of the values. For our set, the mode would be 5, since it occurs 3 times, whereas all other numbers occur only once.
As you can see, the numbers can vary considerably, as can their significance. Therefore, the writer should always inform the reader which average he or she is using. Otherwise, confusion will inevitably ensue.
2. Match your facts with your questions
Be sure that your statistics actually apply to the point/argument you are making. If we return to our discussion of averages, depending on the question you are interesting in answering, you should use the proper statistics.
Perhaps an example would help illustrate this point. Your professor hands back the midterm. The grades are distributed as follows:
The professor felt that the test must have been too easy, because the average (median) grade was a 95.
When a colleague asked her about how the midterm grades came out, she answered, knowing that her classes were gaining a reputation for being “too easy,” that the average (mean) grade was an 80.
When your parents ask you how you can justify doing so poorly on the midterm, you answer, “Don’t worry about my 63. It is not as bad as it sounds. The average (mode) grade was a 58.”
I will leave it up to you to decide whether these choices are appropriate. Selecting the appropriate facts or statistics will help your argument immensely. Not only will they actually support your point, but they will not undermine the legitimacy of your position. Think about how your parents will react when they learn from the professor that the average (median) grade was 95! The best way to maintain precision is to specify which of the three forms of “average” you are using.
3. Show the entire picture
Sometimes, you may misrepresent your evidence by accident and misunderstanding. Other times, however, misrepresentation may be slightly less innocent. This can be seen most readily in visual aids. Do not shape and “massage” the representation so that it “best supports” your argument. This can be achieved by presenting charts/graphs in numerous different ways. Either the range can be shortened (to cut out data points which do not fit, e.g., starting a time series too late or ending it too soon), or the scale can be manipulated so that small changes look big and vice versa. Furthermore, do not fiddle with the proportions, either vertically or horizontally. The fact that USA Today seems to get away with these techniques does not make them OK for an academic argument.
Charts A, B, and C all use the same data points, but the stories they seem to be telling are quite different. Chart A shows a mild increase, followed by a slow decline. Chart B, on the other hand, reveals a steep jump, with a sharp drop-off immediately following. Conversely, Chart C seems to demonstrate that there was virtually no change over time. These variations are a product of changing the scale of the chart. One way to alleviate this problem is to supplement the chart by using the actual numbers in your text, in the spirit of full disclosure.
Another point of concern can be seen in Charts D and E. Both use the same data as charts A, B, and C for the years 1985-2000, but additional time points, using two hypothetical sets of data, have been added back to 1965. Given the different trends leading up to 1985, consider how the significance of recent events can change. In Chart D, the downward trend from 1990 to 2000 is going against a long-term upward trend, whereas in Chart E, it is merely the continuation of a larger downward trend after a brief upward turn.
One of the difficulties with visual aids is that there is no hard and fast rule about how much to include and what to exclude. Judgment is always involved. In general, be sure to present your visual aids so that your readers can draw their own conclusions from the facts and verify your assertions. If what you have cut out could affect the reader’s interpretation of your data, then you might consider keeping it.
4. Give bases of all percentages
Because percentages are always derived from a specific base, they are meaningless until associated with a base. So even if I tell you that after this reading this handout, you will be 23% more persuasive as a writer, that is not a very meaningful assertion because you have no idea what it is based on—23% more persuasive than what?
Let’s look at crime rates to see how this works. Suppose we have two cities, Springfield and Shelbyville. In Springfield, the murder rate has gone up 75%, while in Shelbyville, the rate has only increased by 10%. Which city is having a bigger murder problem? Well, that’s obvious, right? It has to be Springfield. After all, 75% is bigger than 10%.
Hold on a second, because this is actually much less clear than it looks. In order to really know which city has a worse problem, we have to look at the actual numbers. If I told you that Springfield had 4 murders last year and 7 this year, and Shelbyville had 30 murders last year and 33 murders this year, would you change your answer? Maybe, since 33 murders are significantly more than 7. One would certainly feel safer in Springfield, right?
Not so fast, because we still do not have all the facts. We have to make the comparison between the two based on equivalent standards. To do that, we have to look at the per capita rate (often given in rates per 100,000 people per year). If Springfield has 700 residents while Shelbyville has 3.3 million, then Springfield has a murder rate of 1,000 per 100,000 people, and Shelbyville’s rate is merely 1 per 100,000. Gadzooks! The residents of Springfield are dropping like flies. I think I’ll stick with nice, safe Shelbyville, thank you very much.
Percentages are really no different from any other form of statistics: they gain their meaning only through their context. Consequently, percentages should be presented in context so that readers can draw their own conclusions as you emphasize facts important to your argument. Remember, if your statistics really do support your point, then you should have no fear of revealing the larger context that frames them.
Important questions to ask (and answer) about statistics
- Is the question being asked relevant?
- Do the data come from reliable sources?
- Margin of error/confidence interval—when is a change really a change?
- Are all data reported, or just the best/worst?
- Are the data presented in context?
- Have the data been interpreted correctly?
- Does the author confuse correlation with causation?
Now that you have learned the lessons of statistics, you have two options. Use this knowledge to manipulate your numbers to your advantage, or use this knowledge to better understand and use statistics to make accurate and fair arguments. The choice is yours. Nine out of ten writers, however, prefer the latter, and the other one later regrets his or her decision.
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Statistical Methods in Theses: Guidelines and Explanations
Signed August 2018 Naseem Al-Aidroos, PhD, Christopher Fiacconi, PhD Deborah Powell, PhD, Harvey Marmurek, PhD, Ian Newby-Clark, PhD, Jeffrey Spence, PhD, David Stanley, PhD, Lana Trick, PhD
Version: 2.00
This document is an organizational aid, and workbook, for students. We encourage students to take this document to meetings with their advisor and committee. This guide should enhance a committee’s ability to assess key areas of a student’s work.
In recent years a number of well-known and apparently well-established findings have failed to replicate , resulting in what is commonly referred to as the replication crisis. The APA Publication Manual 6 th Edition notes that “The essence of the scientific method involves observations that can be repeated and verified by others.” (p. 12). However, a systematic investigation of the replicability of psychology findings published in Science revealed that over half of psychology findings do not replicate (see a related commentary in Nature ). Even more disturbing, a Bayesian reanalysis of the reproducibility project showed that 64% of studies had sample sizes so small that strong evidence for or against the null or alternative hypotheses did not exist. Indeed, Morey and Lakens (2016) concluded that most of psychology is statistically unfalsifiable due to small sample sizes and correspondingly low power (see article ). Our discipline’s reputation is suffering. News of the replication crisis has reached the popular press (e.g., The Atlantic , The Economist , Slate , Last Week Tonight ).
An increasing number of psychologists have responded by promoting new research standards that involve open science and the elimination of Questionable Research Practices . The open science perspective is made manifest in the Transparency and Openness Promotion (TOP) guidelines for journal publications. These guidelines were adopted some time ago by the Association for Psychological Science . More recently, the guidelines were adopted by American Psychological Association journals ( see details ) and journals published by Elsevier ( see details ). It appears likely that, in the very near future, most journals in psychology will be using an open science approach. We strongly advise readers to take a moment to inspect the TOP Guidelines Summary Table .
A key aspect of open science and the TOP guidelines is the sharing of data associated with published research (with respect to medical research, see point #35 in the World Medical Association Declaration of Helsinki ). This practice is viewed widely as highly important. Indeed, open science is recommended by all G7 science ministers . All Tri-Agency grants must include a data-management plan that includes plans for sharing: “ research data resulting from agency funding should normally be preserved in a publicly accessible, secure and curated repository or other platform for discovery and reuse by others.” Moreover, a 2017 editorial published in the New England Journal of Medicine announced that the International Committee of Medical Journal Editors believes there is “an ethical obligation to responsibly share data.” As of this writing, 60% of highly ranked psychology journals require or encourage data sharing .
The increasing importance of demonstrating that findings are replicable is reflected in calls to make replication a requirement for the promotion of faculty (see details in Nature ) and experts in open science are now refereeing applications for tenure and promotion (see details at the Center for Open Science and this article ). Most dramatically, in one instance, a paper resulting from a dissertation was retracted due to misleading findings attributable to Questionable Research Practices. Subsequent to the retraction, the Ohio State University’s Board of Trustees unanimously revoked the PhD of the graduate student who wrote the dissertation ( see details ). Thus, the academic environment is changing and it is important to work toward using new best practices in lieu of older practices—many of which are synonymous with Questionable Research Practices. Doing so should help you avoid later career regrets and subsequent public mea culpas . One way to achieve your research objectives in this new academic environment is to incorporate replications into your research . Replications are becoming more common and there are even websites dedicated to helping students conduct replications (e.g., Psychology Science Accelerator ) and indexing the success of replications (e.g., Curate Science ). You might even consider conducting a replication for your thesis (subject to committee approval).
As early-career researchers, it is important to be aware of the changing academic environment. Senior principal investigators may be reluctant to engage in open science (see this student perspective in a blog post and podcast ) and research on resistance to data sharing indicates that one of the barriers to sharing data is that researchers do not feel that they have knowledge of how to share data online . This document is an educational aid and resource to provide students with introductory knowledge of how to participate in open science and online data sharing to start their education on these subjects.
Guidelines and Explanations
In light of the changes in psychology, faculty members who teach statistics/methods have reviewed the literature and generated this guide for graduate students. The guide is intended to enhance the quality of student theses by facilitating their engagement in open and transparent research practices and by helping them avoid Questionable Research Practices, many of which are now deemed unethical and covered in the ethics section of textbooks.
This document is an informational tool.
How to Start
In order to follow best practices, some first steps need to be followed. Here is a list of things to do:
- Get an Open Science account. Registration at osf.io is easy!
- If conducting confirmatory hypothesis testing for your thesis, pre-register your hypotheses (see Section 1-Hypothesizing). The Open Science Foundation website has helpful tutorials and guides to get you going.
- Also, pre-register your data analysis plan. Pre-registration typically includes how and when you will stop collecting data, how you will deal with violations of statistical assumptions and points of influence (“outliers”), the specific measures you will use, and the analyses you will use to test each hypothesis, possibly including the analysis script. Again, there is a lot of help available for this.
Exploratory and Confirmatory Research Are Both of Value, But Do Not Confuse the Two
We note that this document largely concerns confirmatory research (i.e., testing hypotheses). We by no means intend to devalue exploratory research. Indeed, it is one of the primary ways that hypotheses are generated for (possible) confirmation. Instead, we emphasize that it is important that you clearly indicate what of your research is exploratory and what is confirmatory. Be clear in your writing and in your preregistration plan. You should explicitly indicate which of your analyses are exploratory and which are confirmatory. Please note also that if you are engaged in exploratory research, then Null Hypothesis Significance Testing (NHST) should probably be avoided (see rationale in Gigerenzer (2004) and Wagenmakers et al., (2012) ).
This document is structured around the stages of thesis work: hypothesizing, design, data collection, analyses, and reporting – consistent with the headings used by Wicherts et al. (2016). We also list the Questionable Research Practices associated with each stage and provide suggestions for avoiding them. We strongly advise going through all of these sections during thesis/dissertation proposal meetings because a priori decisions need to be made prior to data collection (including analysis decisions).
To help to ensure that the student has informed the committee about key decisions at each stage, there are check boxes at the end of each section.
How to Use This Document in a Proposal Meeting
- Print off a copy of this document and take it to the proposal meeting.
- During the meeting, use the document to seek assistance from faculty to address potential problems.
- Revisit responses to issues raised by this document (especially the Analysis and Reporting Stages) when you are seeking approval to proceed to defense.
Consultation and Help Line
Note that the Center for Open Science now has a help line (for individual researchers and labs) you can call for help with open science issues. They also have training workshops. Please see their website for details.
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March 1, 2023
What statistics are most likely to promote positive actions during a pandemic?
by Hailey Reissman, University of Pennsylvania
More information: Haesung Jung et al, How people use information about changes in infections and disease prevalence, Health Psychology (2023). DOI: 10.1037/hea0001262 Journal information: Health Psychology
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Report shows ‘troubling’ rise in colorectal cancer among us adults younger than 55.
Adults across the United States are being diagnosed with colon and rectal cancers at younger ages, and now 1 in 5 new cases are among those in their early 50s or younger, according to the American Cancer Society’s latest colorectal cancer report.
The report says that the proportion of colorectal cancer cases among adults younger than 55 increased from 11% in 1995 to 20% in 2019. There also appears to be an overall shift to more diagnoses of advanced stages of cancer. In 2019, 60% of all new colorectal cases among all ages were advanced.
Colon and rectal cancer cases are going up among people younger than 50, researchers say
“Anecdotally, it’s not rare for us now to hear about a young person with advanced colorectal cancer,” said Dr. William Dahut, chief scientific officer for the American Cancer Society. For example, Broadway actor Quentin Oliver Lee died last year at 34 after being diagnosed with stage IV colon cancer, and in 2020, “Black Panther” star Chadwick Boseman died at 43 of colon cancer.
“It used to be something we never heard or saw this, but it is a high percentage now of colorectal cancers under the age of 55,” Dahut said.
Although it’s difficult to pinpoint a cause for the rise in colorectal cancers among younger adults, he said, some factors might be related to changes in the environment or people’s diets.
“We’re not trying to blame anybody for their cancer diagnosis,” Dahut said. “But when you see something occurring in a short period of time, it’s more likely something external to the patient that’s driving that, and it’s hard not to at least think – when you have something like colorectal cancer – that something diet-related is not impossible.”
Get colon checked sooner, new guidelines say
The new report also says that more people are surviving colorectal cancer, with the relative survival rate at least five years after diagnosis rising from 50% in the mid-1970s to 65% from 2012 through 2018, partly due to advancements in treatment.
That’s good news, said Dr. Paul Oberstein, a medical oncologist at NYU Langone Perlmutter Cancer Center, who was not involved in the new report. The overall trends suggest that colorectal cancer incidence and death rates have been slowly declining.
“If you look at the overall trends, the incidence of colon cancer in this report has decreased from 66 per 100,000 in 1985 to 35 per 100,000 in 2019 – so almost half,” Oberstein said.
“Changes in the mortality rate are even more impressive,” he said. “In 1970, which was a long time ago, the rate of colorectal cancer death was 29.2 per 100,000 people, and in 2020, it was 12.6 per 100,000. So a dramatic, over 55% decline in deaths per 100,000 people.”
Colorectal cancer is the second most common cause of cancer death in the United States, and it is the leading cause of cancer-related deaths in men younger than 50.
Dahut said the best way to reduce your risk of colorectal cancer is to follow screening guidelines and get a stool-based test or a visual exam such as a colonoscopy when it’s recommended. Any suspicious polyps can be removed during a visual exam, reducing your risk of cancer.
“At the ACS, we recommend if you’re at average risk, you start screening at age 45,” Dahut said. “Usually, then your subsequent screening is based on the results of that screening test.”
An ‘alarming’ shift to younger ages
For the new report, researchers at the American Cancer Society analyzed data from the National Cancer Institute and the US Centers for Disease Control and Prevention on cancer screenings, cases and deaths.
The researchers found that from 2011 through 2019, colorectal cancer rates increased 1.9% each year in people younger than 55. And while overall colorectal cancer death rates fell 57% between 1970 and 2020, among people younger than 50, death rates continued to climb 1% annually since 2004.
Half of new colon and rectal cancer diagnoses are now in people age 66 and younger, report finds
“We know rates are increasing in young people, but it’s alarming to see how rapidly the whole patient population is shifting younger, despite shrinking numbers in the overall population,” Rebecca Siegel, senior scientific director of surveillance research at the American Cancer Society and lead author of the report, said in a news release. “The trend toward more advanced disease in people of all ages is also surprising and should motivate everyone 45 and older to get screened.”
Some regions of the United States appeared to have higher rates of colorectal cancers and deaths than others. These rates were lowest in the West and highest in Appalachia and parts of the South and the Midwest, the data showed. The incidence of colorectal cancer ranged from 27 cases per 100,000 people in Utah to 46.5 per 100,000 in Mississippi. Colorectal cancer death rates ranged from about 10 per 100,000 people in Connecticut to 17.6 per 100,000 in Mississippi.
There were some significant racial disparities, as well. The researchers found that colorectal cancer cases and deaths were highest in the American Indian/Alaska Native and Black communities. Among men specifically, the data showed that colorectal cancer death rates were 46% higher in American Indian/Alaska Native men and 44% higher in Black men compared with White men.
Cancer screenings could be back to normal after millions missed during Covid-19 pandemic
The report also says that more left-sided tumors have been diagnosed, meaning an increasing percentage of tumors are happening closer to the rectum. The proportion of colorectal cancers in that location has steadily climbed from 27% in 1995 to 31% in 2019.
“Historically, we’ve been worried more about the tumors on what we call the right side,” said NYU Langone’s Oberstein.
“But the incidence increasing, especially among young people, seems to be happening not only in those worse tumors but the ones that we think are not as bad,” he said, referring to left-sided tumors. “It’s raising questions about whether something is changing about the risks and the future people who are going to get colon cancer.”
Looking forward, the researchers estimate that there will be 153,020 colorectal cancer cases diagnosed in the US this year and an estimated 52,550 colorectal cancer deaths, with 3,750 of them – or 7% – among people younger than 50.
“These highly concerning data illustrate the urgent need to invest in targeted cancer research studies dedicated to understanding and preventing early-onset colorectal cancer,” Dr. Karen Knudsen, CEO of the American Cancer Society, said in the news release. “The shift to diagnosis of more advanced disease also underscores the importance of screening and early detection, which saves lives.”
Screening recommended at 45 and older
The report’s findings, including the rise in colorectal cancer in younger adults, are “troubling,” Dr. Joel Gabre, an expert in gastrointestinal cancers at Columbia University Irving Medical Center, said in an email.
“It reflects other recent published findings demonstrating a rising incidence of colorectal cancer in young people. Most concerning to me, however, is a lack of clear cause and patients being diagnosed late. I think this is an area where more funding for research is needed to understand this really concerning rise,” wrote Gabre, who was not involved in the report.
Gabre says he knows what it’s like to look into his young patients’ eyes and tell them they have colorectal cancer, and “it’s devastating.”
“They have young families and so much of their life ahead of them. That’s why I encourage my patients who are age 45 years and older to get screened,” Gabre said. “I also encourage people to let their doctor know if they have a family history of colon cancer. There is genetic testing we can do to identify some at-risk patients early before they develop cancer.”
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The findings highlight the importance of colorectal cancer screening, Dr. Robin Mendelsohn, gastroenterologist and co-director of the Center for Young Onset Colorectal and Gastrointestinal Cancers at Memorial Sloan Kettering Cancer Center, said in an email.
“The age to start screening was recently decreased to 45, which will help in an effort to screen more people, but we still need to understand more why we are seeing this increase which is something we are actively looking into,” wrote Mendelsohn, was not involved in the new report.
Mendelsohn says she has seen an increase in advanced colorectal cancers and diagnoses among her younger patients, and she says to watch for symptoms such as rectal bleeding, abdominal pain and changes in bowel habits.
“Until we understand more, it is important that patients and providers recognize these symptoms so they can be evaluated promptly,” she said. “And, if you are at an age to get screened, please get screened.”
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Mathematics and Statistics Theses and Dissertations Theses/Dissertations from 2022 PDF New Developments in Statistical Optimal Designs for Physical and Computer Experiments, Damola M. Akinlana PDF Advances and Applications of Optimal Polynomial Approximants, Raymond Centner PDF
Table of contents Step 1: Write your hypotheses and plan your research design Step 2: Collect data from a sample Step 3: Summarize your data with descriptive statistics Step 4: Test hypotheses or make estimates with inferential statistics Step 5: Interpret your results Step 1: Write your hypotheses and plan your research design
Dissertations and Theses in Statistics PhD candidates: You are welcome and encouraged to deposit your dissertation here, but be aware that 1) it is optional, not required (the ProQuest deposit is required); and 2) it will be available to everyone on the Internet; there is no embargo for dissertations in the UNL DigitalCommons.
⊳ Selection from concrete topics provided by the supervisors (see Table 2 for an exemplary list), where relevant publication(s) and related media reports are also given. Remark 3.1. Due to the worldwide pandemic situation during the summer term 2020, a special focus was placed on COVID-19 with topics like "Infection pathways of the coronavirus" and "Myths and facts about COVID-19 ...
A selection of Mathematics PhD thesis titles is listed below, some of which are available online: 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991. 2021. Jennifer E. Israelsson - The spatial statistical distribution for multiple rainfall intensities over Ghana Giulia Carigi - Ergodic properties ...
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Statistics thesis is a complex document which requires interpretation, analysis and knowledge of all aspects of data. The task requires a lot of quality and authentic research material. Researchomatic has therefore dedicated an entire section to statistics thesis, which will help students to gain useful ideas about their own work.
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Master's Thesis | Statistical Science Master's Thesis As part of the Completion Exercise for the Master's of Science in Statistical Science degree, you may write and present your Master's Thesis. This oral examination is administered by your Master's Committee.
One of the reasons to use statistics is to condense large amounts of information into more manageable chunks; presenting your entire data set defeats this purpose. At the bare minimum, if you are presenting statistics on a data set, it should include the mean and probably the standard deviation.
How To Write Good Statistics Research Topics? Top 3 Tips to Choose Good Statistics Research Topics 1. Narrow down your research topic 2. Choose a topic that gives you curiosity 3. Choose a manageable topic Conclusion Frequently Asked Questions Q1. What are some good topics for the Statistics project? Q2. What are the topics in statistics? Q3.
DStat thesis: Challenges in modelling pharmacogenetic data: Investigating biomarker and clinical response simultaneously for optimal dose prediction. Rungruttikarn Moungmai. Family-based genetic association studies in a likelihood framework. Michael Dunbar. Multiple hydro-ecological stressor interactions assessed using statistical models.
Statistics in Research. Definition of statistics: Statistics is the science of learning from data, and of measuring, controlling, and communicating uncertainty; and it thereby provides the navigation essential for controlling the course of scientific and societal advances (Davidian, M. and Louis, T. A., 10.1126/science.1218685).
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Guidelines and Explanations. In light of the changes in psychology, faculty members who teach statistics/methods have reviewed the literature and generated this guide for graduate students. The guide is intended to enhance the quality of student theses by facilitating their engagement in open and transparent research practices and by helping ...
"Goodness-of-fit statistics based on phi-divergences" Leah R. Jager: Jon A Wellner: 2005 Title Author Supervisor "Alternative models for estimating genetic maps from pedigree data" William Charles Stewart: Elizabeth Thompson "Allele-sharing methods for linkage detection using extended pedigrees" ...
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Statistical Analysis. Included in the methodology section should be a thorough explanation of data and the methods by which data was obtained. Instruments of data collection vary, but common methods include surveys, interviews, questionaires, and case studies. The writer must show methodological expertise through analyzing the benefits and ...
Throughout the COVID-19 pandemic, health officials updated the public on the outbreak through statistics—case counts, vaccination rates, test distribution. Whether displayed through graphs ...
A thesis (plural: theses), or dissertation (abbreviated diss.), is a document submitted in support of candidature for an academic degree or professional qualification presenting the author's research and findings. In some contexts, the word thesis or a cognate is used for part of a bachelor's or master's course, while dissertation is normally applied to a doctorate.
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The report says that the proportion of colorectal cancer cases among adults younger than 55 increased from 11% in 1995 to 20% in 2019. There also appears to be an overall shift to more diagnoses ...