In: Math
A psychologist is planning a study to test whether people are more likely to call events "natural" that are otherwise frequently attributed to "supernatural forces" if they have seen a particular film critical of "superstitions." Discuss the three things the psychologist should consider in order to maximize power for the planned study. Be specific, i.e., describe the statistical principles and explain how each works to increase power
Increase the power of a hypothesis test
You can use any of the following methods to increase the power of a hypothesis test.
Using a larger sample provides more information about the population and, thus, increase power. Using a larger sample is often the most practical way to increase power.
For a hypothesis test of means (1-sample Z, 1-sample t, 2-sample t, and paired t), improving your process decreases the standard deviation. When the standard deviation is smaller, the power increases and smaller differences can be detected.
Using a higher significance level increases the probability that you reject the null hypothesis. However, be cautious, because you do not want to reject a null hypothesis that is actually true. (Rejecting a null hypothesis that is true is called type I error.)
It is easier to detect larger differences in population means.
A directional hypothesis has more power to detect the difference that you specify in the direction that you specify. (The direction is either less than or greater than.) However, be cautious, because a directional hypothesis cannot detect a difference that is in the opposite direction.
Increase the power of an ANOVA
You can use any of the following methods to increase the power of a one-way ANOVA.
Using a larger sample provides more information about the population and, thus, increase power. Using a larger sample is often the most practical way to increase power.
It is easier to detect larger differences in population means.
Improving your process decreases the standard deviation and, thus, increases power.
Using a higher significance level increases the probability that you reject the null hypothesis. However, be cautious, because you do not want to reject a null hypothesis that is actually true. (Rejecting a null hypothesis that is true is called type I error.)