Activity Menu
A great way to learn is through play. Explore the single sample t test with this interactive app (25–40 minutes). Those interested may do these activities alongside the optional video tutorial (opens in a new tab). Complete the review, the three guided activities and the Test Your Understanding activity (click on a link below to get started).
- Hypothesis Testing Review (not yet completed)
- The Hypothesis Decision Rule (and Matrix) (not yet completed)
- The t Test (not yet completed)
- Communicating the Results APA Style (not yet completed)
- Test Your Understanding (not yet completed)
At any time, explore the relationship between ‘The Reality’, ‘The Researcher’s Perspective’, and ‘Communicating the Results APA Style’ by using the ‘Scenario Controls (Self-guided Exploration)’. Or, explore the topic of ‘Statistical Power’. Finally, learn more ‘About the app’.
The Hypothesis Decision Rule (and Matrix) Guided Tour
The primary purpose of this app is to help you visualize scenarios whose probability (i.e., the p-value) is either very large or very small. For the researcher engaged in hypothesis testing, it is the low probability scenarios that are the most exciting. These low probability scenarios look just like a real treatment effect (μT ≠ μ0), yet they can result from random selection (i.e., sampling error). To see why researchers pay close attention to p-values when deciding whether to reject the null hypothesis, click on each of the probabilities below — the app will automatically configure the scenario.
Try each of the scenarios (by clicking the link below):
- (No treatment effect; the sample mean equals the population mean)
- (Large treatment effect; the sample mean is far from the population mean)
After checking out both scenarios, two additional options will appear below — Type One Error and Type Two Error.
Hypothesis Testing Decision Matrix (Probability)
For those interested, dive deeper into the Hypothesis Decision Matrix, by switching between the no treatment effect (the null hypothesis is true) and an actual treatment effect (the null hypothesis is false). For a true null, μ0 = 25, any difference between the sample mean and the population mean, M − μ0, is entirely random. For both scenarios (with or without a treatment effect), click or hold down the ‘Random p’ button to generate several examples to explore the probabilities of Type One and Type Two Errors, and Correct Decisions.
After changing the sample size (though holding the sample mean and variance constant), see the impact on the current scenario. Adjust the p-value for a new scenario.