Chapter 10: Hypothesis Testing
Student Resources
I use the 4 “P’s” framework to help you learn the material in this chapter: Prepare, Practice, Participate, and Perform. To increase the chances to succeed in this course, I strongly encourage you to complete all four “P’s” for each chapter.
1 Prepare
1.1 Chapter Overview
This chapter introduces hypothesis testing—a formal framework for making decisions about populations based on sample data. You’ll learn about null and alternative hypotheses, p-values, Type I and Type II errors, statistical power, and how to conduct and interpret t-tests in movement science research.
1.2 Multimedia Resources
The following table provides access to video and slide resources for this chapter. Click the links to open them in an overlay for better viewing on all devices.
| Resource | Description | Link |
|---|---|---|
| Long Video Overview | A detailed video explaining the logic of hypothesis testing, p-values, and how to conduct t-tests in movement science research. | 🔗 Watch Video |
| Slide Overview PDF | PDF slides that serve as an overview of this chapter. Read these before the textbook to introduce the main concepts and vocabulary. | 🔗 Download PDF |
| Slide Deck HTML | Interactive HTML slides for class. During class, the instructor controls the presentation; after class, review at your own pace. | 🔗 Open Slides |
| Slide Deck PDF | PDF version of the slide deck for download and offline viewing. | 🔗 Download PDF |
1.3 Read the Chapter
Read (Furtado, 2026, p. Ch10) and (Weir & Vincent, 2021, p. Ch.7) to understand the logic of hypothesis testing, p-values, and statistical decision-making.
To succeed in this course, you must read the textbook chapters assigned for each topic. This is the only way to learn the material in depth.
Once done, proceed to the next section to practice what you learned.
2 Practice
Practicing what you learned in the chapter is essential to mastering the material. Below are some resources to help you practice the material in this chapter.
2.1 Frequently Asked Questions
Hypothesis testing is a formal statistical procedure for making decisions about populations based on sample data. We test a specific claim (null hypothesis) by determining whether our sample data provide sufficient evidence to reject that claim.
The null hypothesis (H₀) is a statement of “no effect,” “no difference,” or “no relationship.” It represents the status quo or the assumption that nothing unusual is happening. Example: “The mean vertical jump height is 50 cm” (H₀: μ = 50).
The alternative hypothesis (H₁ or Hₐ) is a statement that contradicts the null hypothesis. It represents the effect, difference, or relationship we are testing for. Example: “The mean vertical jump height is not 50 cm” (H₁: μ ≠ 50).
The p-value is the probability of observing data as extreme as (or more extreme than) what we actually observed, assuming the null hypothesis is true. Small p-values (typically p < 0.05) suggest the data are inconsistent with the null hypothesis.
A p-value of 0.03 means: “If the null hypothesis were true, there is a 3% chance we would observe data this extreme by random chance alone.” It does not mean there’s a 3% chance the null hypothesis is true.
The significance level (α) is the threshold we set for deciding whether to reject the null hypothesis. The conventional value is α = 0.05, meaning we’re willing to accept a 5% risk of falsely rejecting a true null hypothesis.
A Type I error (false positive) occurs when we reject the null hypothesis when it is actually true. The probability of Type I error equals the significance level (α). Example: Concluding a training program works when it actually doesn’t.
A Type II error (false negative) occurs when we fail to reject the null hypothesis when the alternative hypothesis is actually true. Symbol: β (beta). Example: Concluding a training program doesn’t work when it actually does.
Statistical power is the probability of correctly rejecting the null hypothesis when the alternative is true (Power = 1 − β). It represents the ability of a study to detect a real effect. Aim for Power ≥ 0.80 (80%).
Power is affected by: - Sample size: Larger n → higher power - Effect size: Larger effects → higher power - Significance level: Higher α → higher power (but more Type I errors) - Variability: Lower variability → higher power
Two-tailed tests check for any difference in either direction (H₁: μ ≠ μ₀). One-tailed tests check for a difference in a specific direction (H₁: μ > μ₀). Use two-tailed tests by default unless you have strong theoretical reasons for a directional hypothesis.
Use a t-test to compare means: - One-sample t-test: Compare sample mean to a known value - Independent t-test: Compare means between two independent groups - Paired t-test: Compare means for the same group measured twice
Statistically significant means the p-value is less than the significance level (p < α), so we reject the null hypothesis. It indicates the effect is unlikely to be due to chance alone, but does not mean the effect is large or practically important.
Statistical significance (p < α) tells us whether an effect is detectable and unlikely due to chance. Practical significance evaluates whether the effect is large enough to matter in real-world contexts. Always consider both.
Failing to reject H₀ means the data are compatible with the null hypothesis—we lack sufficient evidence against it. It does not mean H₀ is true or proven. The study may simply lack power to detect a real effect.
We can never prove H₀ is true from sample data—we can only fail to find evidence against it. Absence of evidence is not evidence of absence. Use confidence intervals to assess the range of plausible values.
2.2 Test your Knowledge
Take this low-stakes quiz to test your knowledge of the material in this chapter. This quiz is for practice only and will help you identify areas where you may need additional review.
3 Participate
This section includes activities and discussions that will be completed during class time. Your active participation is essential for deepening your understanding of the material.
During class, we will:
- Formulate null and alternative hypotheses for research questions
- Calculate and interpret p-values
- Conduct one-sample, independent, and paired t-tests
- Discuss the meaning of statistical vs. practical significance
- Explore Type I and Type II errors through examples
4 Perform
4.1 Apply Your Learning
Now that you’ve prepared, practiced, and participated, it’s time to demonstrate your mastery of the material through assignments and assessments.
I strongly encourage you to complete the previous “Ps” (Prepare, Practice, Participate) before attempting any assignments or assessments associated with this chapter.