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## A Beginner’s Guide to Hypothesis Testing in Business

Access your free e-book today.

## What Is Hypothesis Testing?

To understand what hypothesis testing is, it’s important first to understand what a hypothesis is.

## Hypothesis Testing in Business

Related: 9 Fundamental Data Science Skills for Business Professionals

## Key Considerations for Hypothesis Testing

1. alternative hypothesis and null hypothesis.

## 2. Significance Level and P-Value

## 3. One-Sided vs. Two-Sided Testing

## 4. Sampling

## Learning How to Perform Hypothesis Testing

## About the Author

## 3 Key Characteristics of Well-Formed Hypotheses

Written by Tendayi Viki on December 03, 2019

- An assumption that your value proposition, business model, or strategy builds on.
- All the things that would have to be true for you business idea to work.

- Generation Z → Adults between 18-24
- Prefers → spend more time… compared to
- pop-up store → temporary pop-up stores that are placed in co-working spaces
- Branches → traditional banking branches

The hypothesis on the right side ensures that the team are all testing the same thing:

- Young adults → majority of young adults between 18-24
- Plan → don’t save more than $100 per month
- Future → retirement

The second hypothesis is about the viability of your idea: Can it save money?

Learn how to reduce risk and uncertainty of new business ideas.

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## HYPOTHESIS TESTING

Hypotheses provide the following benefits:

- They determine the focus and direction for a research effort.
- Their development forces the researcher to clearly state the purpose of the research activity.
- They determine what variables will not be considered in a study, as well as those that will be considered.
- They require the researcher to have an operational definition of the variables of interest.

- Have logical consistency. Based on the current research literature and knowledge base, does this hypothesis make sense?
- Be in step with the current literature and/or provide a good basis for any differences. Though it does not have to support the current body of literature, it is necessary to provide a good rationale for stepping away from the mainstream.
- Be testable. If one cannot design the means to conduct the research, the hypothesis means nothing.
- Be stated in clear and simple terms in order to reduce confusion.

## HYPOTHESIS TESTING PROCESS

- State the hypotheses of interest
- Determine the appropriate test statistic
- Specify the level of statistical significance
- Determine the decision rule for rejecting or not rejecting the null hypothesis
- Collect the data and perform the needed calculations
- Decide to reject or not reject the null hypothesis

## STATING THE HYPOTHESES.

## DETERMINING THE APPROPRIATE TEST STATISTIC.

For example, the appropriate statistic to use when testing a hypothesis about a population means is:

## SPECIFYING THE STATISTICAL SIGNIFICANCE SEVEL.

## DETERMINING THE DECISION RULE.

## COLLECTING THE DATA AND PERFORMING THE CALCULATIONS.

## DECIDING WHETHER TO REJECT THE NULL HYPOTHESIS.

- State the hypotheses. H 0 = 20 years H 1 ≠ 20 years
- Determine the test statistic. Since we are testing a population mean that is normally distributed, the appropriate test statistic is:
- Specify the significance level. Since the firm would like to keep its present message to new recruits, it selects a fairly weak significance level (α = .05). Since this is a two-tailed test, half of the alpha will be assigned to each tail of the distribution. In this situation the critical values of Z = +1.96 and −1.96.
- State the decision rule. If the computed value of Z is greater than or equal to +1.96 or less than or equal to −1.96, the null hypothesis is rejected.
- Calculations.
- Reject or fail to reject the null. Since 2.5 is greater than 1.96, the null is rejected. The mean tenure is not 20 years, therefore XYZ needs to change its statement.

SEE ALSO: Research Methods and Processes ; Statistics

## FURTHER READING:

## Other articles you might like:

- What is Strategy?
- Business Models
- Developing a Strategy
- Strategic Planning
- Competitive Advantage
- Growth Strategy
- Market Strategy
- Customer Strategy
- Geographic Strategy
- Product Strategy
- Service Strategy
- Pricing Strategy
- Distribution Strategy
- Sales Strategy
- Marketing Strategy
- Digital Marketing Strategy
- Organizational Strategy
- HR Strategy – Organizational Design
- HR Strategy – Employee Journey & Culture
- Process Strategy
- Procurement Strategy
- Types of Value
- Competitive Dynamics
- Problem Solving
- Strategic Options
- Decision Making
- Process Tools
- Planning and Projects
- People Leadership
- Personal Development
- OnDemand Coaching

– Edward Teller, Nuclear Physicist

## What is a Hypothesis?

Let’s go over an example of being hypothesis-driven.

One of my favorite lousy logic moments was a CEO who stated,

## Why is being hypothesis-driven so important?

## How do you become hypothesis-driven?

## Listen to Your Intuition

## Constantly Be Curious

## Validate Hypotheses

## Be a Learning Organization

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## BOOK SOME ONE-ON-ONE TIME WITH JOE

## EXPLORE OTHER TYPES OF STRATEGIES

BIG PICTURE WHAT IS STRATEGY? BUSINESS MODEL COMP. ADVANTAGE GROWTH

TARGETS MARKET CUSTOMER GEOGRAPHIC

VALUE PROPOSITION PRODUCT SERVICE PRICING

GO TO MARKET DISTRIBUTION SALES MARKETING

ORGANIZATIONAL ORG DESIGN HR & CULTURE PROCESS PARTNER

## EXPLORE THE TOP 100 STRATEGIC LEADERSHIP COMPETENCIES

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OPTION CREATION ANALYTICS DECISION MAKING PROCESS TOOLS

PLANNING & PROJECTS PEOPLE LEADERSHIP PERSONAL DEVELOPMENT

## CLICK HERE FOR TONS OF FREE STRATEGY & LEADERSHIP TEMPLATES

## 4 Examples of Hypothesis Testing in Real Life

- Null Hypothesis (H 0 ): The sample data occurs purely from chance.
- Alternative Hypothesis (H A ): The sample data is influenced by some non-random cause.

The following examples provide several situations where hypothesis tests are used in the real world.

## Example 1: Biology

She then performs a hypothesis test using the following hypotheses:

- H 0 : μ = 20 inches (the fertilizer will have no effect on the mean plant growth)
- H A : μ > 20 inches (the fertilizer will cause mean plant growth to increase)

## Example 2: Clinical Trials

He then performs a hypothesis test using the following hypotheses:

- H 0 : μ after = μ before (the mean blood pressure is the same before and after using the drug)
- H A : μ after < μ before (the mean blood pressure is less after using the drug)

## Example 3: Advertising Spend

They may perform a hypothesis test using the following hypotheses:

- H 0 : μ after = μ before (the mean sales is the same before and after spending more on advertising)
- H A : μ after > μ before (the mean sales increased after spending more on advertising)

## Example 4: Manufacturing

They can then perform a hypothesis test using the following hypotheses:

- H 0 : μ after = μ before (the mean number of defective widgets is the same before and after using the new method)
- H A : μ after ≠ μ before (the mean number of defective widgets produced is different before and after using the new method)

## Additional Resources

## Published by Zach

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## Chapter 4. Hypothesis Testing

## The strategy of hypothesis testing

H o : proportion of the population recognizing Pedal Pushers brand < .30

H a : proportion of the population recognizing Pedal Pushers brand >.30

## A straightforward example: testing for goodness-of-fit

H o : Chargers players’ sock sizes are distributed just like current production.

H a : Chargers players’ sock sizes are distributed differently.

[latex]sample\;chi^2 = \sum{((O-E)^2)/E}[/latex]

O = observed frequency in the sample in this class

E = expected frequency in the sample in this class

## Testing population proportions

[latex]z = (p-\pi)/\sqrt{\dfrac{(\pi)(1-\pi)}{n}}[/latex]

p = the proportion of the sample with a certain characteristic

π = the proportion of the population with that characteristic

[latex]\pi = 826/2483 = .333[/latex]

Using this value, he computes his sample z-statistic:

## Testing independence and categorical variables

The owner writes her hypotheses:

H o : Customers’ preferences for dishes are independent of their ages

H a : Customers’ preferences for dishes depend on their ages

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## How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on December 2, 2022.

## Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

## Table of contents

## Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

- An independent variable is something the researcher changes or controls.
- A dependent variable is something the researcher observes and measures.

## Step 1. Ask a question

## Step 2. Do some preliminary research

## Step 3. Formulate your hypothesis

## 4. Refine your hypothesis

- The relevant variables
- The specific group being studied
- The predicted outcome of the experiment or analysis

## 5. Phrase your hypothesis in three ways

## 6. Write a null hypothesis

- H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
- H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

## Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

## Cite this Scribbr article

McCombes, S. (2022, December 02). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved February 28, 2023, from https://www.scribbr.com/methodology/hypothesis/

## IMAGES

## VIDEO

## COMMENTS

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Hypothesis testing can be used in business applications to help validate an assumption being made about data relationships. This lesson looks at...