What is the significance of setting a significance level in hypothesis testing?

The significance level in hypothesis testing, often denoted by alpha (α), serves a crucial role in determining the threshold for making decisions about the null hypothesis. When researchers set a significance level, they are specifying the acceptable probability of making a Type I error, which occurs when the null hypothesis is incorrectly rejected. For instance, if a researcher sets α at 0.05, this implies that there is a 5% risk of concluding that there is an effect or difference when, in fact, none exists.

By clearly establishing this threshold, researchers can interpret their p-values in the context of the significance level. If the p-value obtained from their analysis is less than or equal to the significance level, they reject the null hypothesis, indicating that their findings are statistically significant. This setting helps maintain scientific rigor by controlling the possibility of falsely claiming support for an effect that is not present.

In contrast, other choices do not align with the primary function of the significance level. For example, determining the sample size or selecting participants relates to study design but does not directly pertain to the assessment of hypothesis testing errors. Similarly, establishing the direction of research is about formulating hypotheses rather than the specific decision-making process related to statistical significance. Thus, option B captures