#### WISE Power Tutorial – Discussion Questions for Statistical Power

Your friend Bumble plans to conduct a study to determine whether people who are exposed to an advertisement for a power drink have a more favorable view of that product compared to people not exposed to the advertisement. He has some questions and asks for your help.

**1.** Bumble says, “I know that statistical power is supposed to be larger if I increase the sample size, but I don’t understand why. Can you explain why sample size is related to power?”

**2. **Bumble asks you, “Would a sample of 50 people be large enough?” How do you respond? What more do you need to know and why?

**3. **Bumble says, “I really would like to design my study to be able to show that my advertisement is effective. How many cases do I need to make SURE I can reject the null hypothesis?” How do you respond?

**4. **Bumble says, “I don’t want to make an alpha error where I conclude that my advertisement is effective when it really isn’t effective. If I set my alpha error smaller (.001 instead of .05), will that give me much more power?” Explain to Bumble how alpha error is related to power.

**5. **Bumble says, “I think the advertisement will be more effective if I show it to people three times instead of just once.” If Bumble is right, how would power be affected?

**6. **Bumble conducted a study of his advertisement for a power drink, and he found that those who saw the advertisement had a significantly more favorable view of the product than those who did not see the advertisement, p = .001. Bumble concluded that he had strong evidence that the advertisement produced a very large and important effect. Evaluate Bumble’s conclusion and interpret his findings.

*(Hint: Consider the distinctions between statistical significance, practical significance, and large effect size. How is sample size related to statistical power? Is a statistically significant effect necessarily an important effect? Can one obtain a statistically significant effect with a small effect?)*