Understanding Variables and Hypothesis Testing
Learn the foundational concepts of quantitative research: defining variables, forming testable hypotheses, and choosing the right statistical tests.
Understanding variables and hypotheses is fundamental to conducting meaningful quantitative research. This guide expands on concepts introduced in our complete Guide to Quantitative Research.
At the heart of all quantitative research is a simple idea: testing a relationship between variables to see if it's real or just due to chance.
This process, known as hypothesis testing, is the scientific method applied to business decisions. It's how you move from 'I think' to 'I know'.
This guide breaks down these core concepts into simple, understandable terms, providing the foundation for sound quantitative analysis.
Independent vs. Dependent Variables
All hypothesis testing revolves around understanding the relationship between these two types of variables.
This is the 'cause' in a cause-and-effect relationship. It's the factor you control to see what impact it has on the outcome.
Example:
In an A/B test, the color of a button (blue vs. green) is the independent variable.
This is the 'effect' you are watching for. Its value depends on the changes you make to the independent variable.
Example:
The click-through rate of the button is the dependent variable.
You change the independent variable...
...to see the effect on the dependent variable.
How to Form a Testable Hypothesis
A hypothesis is a specific, testable prediction about what you expect to happen in your study.
Ask a Question
Start with a broad question that stems from a business problem or an observation.
Example: 'How can we increase user sign-ups?'
Do Background Research
Gather existing information (secondary research) to refine your question.
Example: 'Analytics show users drop off on the pricing page.'
Formulate a Null Hypothesis (H₀)
State that there is no relationship or difference between the variables. This is the default assumption that you will try to disprove.
Example (H₀): 'Changing the button color will have no effect on the sign-up rate.'
Formulate an Alternative Hypothesis (H₁)
State the relationship or difference you expect to find. This is your actual prediction.
Example (H₁): 'Changing the button color from blue to green will increase the sign-up rate.'
Make it Testable
Ensure your hypothesis is specific enough to be tested with data.
Example: The variables (button color, sign-up rate) are clear and measurable.
Common Statistical Tests for Hypothesis Testing
Once you have your hypothesis, you need the right statistical tool to test it. Here are three common ones.
Use Case:
Comparing the average purchase value of customers who received discount code A vs. discount code B.
Use Case:
Comparing the effectiveness of three different ad creatives on user engagement.
Use Case:
Predicting house prices (dependent) based on features like square footage and number of bedrooms (independent).
Hypothesis Testing FAQs
Common questions about variables, hypotheses, and statistical tests.
Ready to Test Your Assumptions?
Download our free Hypothesis Testing Worksheet to help you structure your next quantitative study.