What does the null hypothesis assume in statistical testing?

The null hypothesis is a fundamental concept in statistical testing that serves as a starting assumption to evaluate the evidence against it. When formulating a null hypothesis, it typically posits that there is no effect or no difference between groups or models being analyzed. In the context of comparing models, stating that there is no statistically significant difference means that any observed effect or difference in the analysis could easily be attributed to random variation or sampling error rather than a true effect.

This assumption allows researchers to perform tests to determine whether the data provide sufficient evidence to reject the null hypothesis in favor of an alternative hypothesis, which suggests that there is indeed a significant effect or difference. The significance testing framework, which often relies on p-values, helps quantify this assessment.

Other choices suggest premises that do not align with the foundational concept of the null hypothesis: stating a significant difference (the first option) directly contradicts the null hypothesis's central claim, and suggesting an inverse relationship (the third option) introduces a specific directional alternative hypothesis. The last choice regarding normality pertains to assumptions of data distribution rather than directly describing the role of the null hypothesis in hypothesis testing.