What does a 'p-value' signify in hypothesis testing?

A 'p-value' serves a critical role in hypothesis testing by indicating the probability of observing the results from a study, or more extreme outcomes, under the assumption that the null hypothesis is true. Essentially, it helps researchers determine whether the observed data is consistent with what would be expected if the null hypothesis were indeed valid. A low p-value suggests that such extreme results would be unlikely under the null hypothesis, leading researchers to consider rejecting the null in favor of the alternative hypothesis.

In contrast, while the other options touch upon relevant concepts in research, they do not accurately capture the function of the p-value in hypothesis testing. For instance, the likelihood of a false positive result pertains to the concept of Type I error, but is not the definition of a p-value itself. The power of a study is related to its ability to detect an effect if one exists, while the average expected effect size deals with what the research anticipates finding in the population rather than assessing the data's extremity regarding the null hypothesis. Thus, the correct answer clearly identifies the primary purpose of the p-value in the statistical analysis framework.