William H. Knapp III

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1. Review: Imagine a statistical test that could result in 3 different outcomes. If you performed the experiment 20 times, how many different events could you observe?

2. Review: How many ways could you arrange 12 objects?

3. Review: How many ways could you arrange 5 of 12 objects?

4. Review: How many combinations can you get when choosing 4 out of 16 students?

5. When calculating p-values for a null hypothesis like p(success)=.7, which probabilities from the probability mass function should you add up?
Probabilities from as many or fewer successes than what was observed.
Probabilities from as many or more successes than what was observed.
Probabilities from as many, fewer, or more successess than what was observed.
Probabilities greater than or equal to the probability of that many observed successes.
Probabilities less than or equal to the probability of that many observed successes.

6. When calculating p-values for a null hypothesis like p(success)>=.7, which probabilities from the probability mass function should you add up?
Probabilities from as many or fewer successes than what was observed.
Probabilities from as many or more successes than what was observed.
Probabilities from as many, fewer, or more successess than what was observed.
Probabilities greater than or equal to the probability of that many observed successes.
Probabilities less than or equal to the probability of that many observed successes.

7. When calculating p-values for a null hypothesis like p(success)<=.7, which probabilities from the probability mass function should you add up?
Probabilities from as many or fewer successes than what was observed.
Probabilities from as many or more successes than what was observed.
Probabilities from as many, fewer, or more successess than what was observed.
Probabilities greater than or equal to the probability of that many observed successes.
Probabilities less than or equal to the probability of that many observed successes.

8. When calculating p-values for a null hypothesis like p(success)<=.13, which tail holds alpha?
The tail containing fewer than the expected number of successes.
The tail containing more than the expected number of successes.
Tails on both sides of the expected number of successes.
There is not enough information to tell.

9. When calculating p-values for a null hypothesis like p(success)>=.52, which tail holds alpha?
The tail containing fewer than the expected number of successes.
The tail containing more than the expected number of successes.
Tails on both sides of the expected number of successes.
There is not enough information to tell.

10. When calculating p-values for a null hypothesis like p(success)=.85, which tail holds alpha?
The tail containing fewer than the expected number of successes.
The tail containing more than the expected number of successes.
Tails on both sides of the expected number of successes.
There is not enough information to tell.

11. Alpha errors occur when.
You fail to reject the null hypothesis when it is false.
You fail to reject the null hypothesis when it is true.
You reject the null hypothesis when it is false.
You reject the null hypothesis when it is true.
There is not enough information to tell.

12. Beta errors occur when.
You fail to reject the null hypothesis when it is false.
You fail to reject the null hypothesis when it is true.
You reject the null hypothesis when it is false.
You reject the null hypothesis when it is true.
There is not enough information to tell.

13. Holding everything else constant, increasing alpha generally decreases power.
True
False
There is not enough information to tell.

14. Holding everything else constant, using a directional hypothesis instead of a non-directional hypothesis generally increases power.
True
False
There is not enough information to tell.

15. Holding everything else constant, increasing the sample size generally increases alpha.
True
False
There is not enough information to tell.

16. Holding everything else constant, increasing the sample size generally increases beta.
True
False
There is not enough information to tell.

17. Holding everything else constant, with a larger effect size there is generally more power.
True
False
There is not enough information to tell.

18. What is power? Choose all that apply.
1-Beta
Beta
The probability of correctly rejecting the null hypothesis when some given specific alternative is true.
The probability of correctly failing to reject the null hypothesis when some given specific alternative is true.
The probability of incorrectly rejecting the null hypothesis when some given specific alternative is true.
The probability of incorrectly failing to reject the null hypothesis when some given specific alternative is true.

19. Power increases as Beta decreases.
True
False
There is not enough information to tell.

20. When you calculate the power you would have to detect some effect before you collect any data, you are doing what type of analysis?
a priori
a posteriori
ad absurdum
ad hominem
There is not enough information to tell.