When computing inferential statistics,we usually want to know the likelihood of getting results:
A)exactly like those we observed - based on chance
B)as extreme as or more extreme than those we observed - based on chance
C)as significant as ours - based on chance
D)exactly like ours if the null hypothesis is false

Question

Quizplus · Accepted Answer

When computing inferential statistics, we usually want to know the likelihood of getting results as extreme as or more extreme than what we observed based on chance. This helps us determine the statistical significance of our results and whether they are likely due to chance or to a true effect in the population. Option A refers to the probability of getting exact results, which is unlikely, and Option C refers to statistical significance, which is related but not synonymous with the likelihood of chance results. Option D refers to a scenario where the null hypothesis is false, which is not always the case, and thus not the most appropriate choice for general inference testing purposes.

When Computing Inferential Statistics,we Usually Want to Know the Likelihood