Statistical tests are primarily designed to reject which hypothesis?

Statistical tests are fundamentally structured to evaluate the evidence against the null hypothesis, which is often a statement asserting that there is no effect or no difference between groups or conditions in the context of the study. The null hypothesis serves as a baseline or a starting point for statistical inference.

When conducting a statistical test, researchers calculate a test statistic and a corresponding p-value to determine the likelihood of observing the collected data, or something more extreme, if the null hypothesis were true. If the p-value falls below a predetermined significance level, we have enough statistical evidence to reject the null hypothesis. This does not prove that the null hypothesis is false, but rather suggests that the data provides evidence against it, supporting the possibility of an alternate hypothesis.

The alternate hypothesis represents the opposite of the null hypothesis, suggesting that there is a significant effect or difference. Rejecting the null hypothesis supports the alternate hypothesis. The conjectural and inductive hypotheses, while relevant in other contexts of hypothesis formulation, are not the primary focal points in standard statistical testing procedures where the null hypothesis is the primary target for rejection.