Post-Assessment: Hypothesis Testing

Measure how much you've learned!

Measure Your Learning Gains!

You took a pre-assessment before starting this module. Now you'll answer the same 5 questions to see how much you've learned. This helps both you and us understand the effectiveness of the learning materials!

This is still not graded—it's purely to measure learning progress.

Question 1

What is the purpose of hypothesis testing?

To prove that a hypothesis is true
To determine if there is enough evidence to reject a null hypothesis
To calculate the exact value of a population parameter
To create confidence intervals

Question 2

The null hypothesis (H₀) typically represents:

The alternative claim we want to prove
The status quo or claim of no effect
The research hypothesis
A hypothesis that is always false

Question 3

A Type I error occurs when:

We reject a true null hypothesis
We fail to reject a false null hypothesis
We calculate the test statistic incorrectly
We use the wrong significance level

Question 4

If a p-value is 0.03 and the significance level (α) is 0.05, we should:

Fail to reject the null hypothesis
Reject the null hypothesis
Accept the alternative hypothesis
Increase the sample size

Question 5

When testing a claim about a population mean with unknown population standard deviation, which test should you use?

z-test
t-test
chi-square test
ANOVA

Post-Assessment Complete!

Your Learning Gains

Module 8 Complete!

Congratulations on completing Module 8: Hypothesis Testing! You've learned the foundation of statistical inference used in research across all fields.

You can now:

Formulate null and alternative hypotheses
Understand Type I and Type II errors and statistical power
Conduct z-tests and t-tests for means
Perform hypothesis tests for proportions
Make data-driven decisions with statistical rigor

Recommended Next Steps

Download the comprehensive study guide
Save the quick reference card for future use
Continue to the next module to build on these skills
Practice applying hypothesis testing to real-world data

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