What characterizes a null hypothesis?

A null hypothesis is a fundamental concept in statistical hypothesis testing. It is specifically formulated to state that there is no relationship or effect between the variables being studied. More precisely, it posits that any observed differences or effects in the data are the result of random chance rather than a true effect of the independent variable on the dependent variable.

This is why the characterization of the null hypothesis as stating that any observed effect is due to chance is correct. In hypothesis testing, the null hypothesis serves as the baseline against which the alternative hypothesis is tested. The goal of statistical tests is often to gather enough evidence to reject the null hypothesis in favor of the alternative, which suggests a significant effect or relationship.

The other options present notions that do not accurately describe the null hypothesis. For instance, stating that it asserts there is a significant difference contradicts its fundamental nature, which is to suggest that no such difference exists. Similarly, predicting an impact of the independent variable on the dependent variable aligns with the alternative hypothesis, not the null. Additionally, while a high level of certainty might be a characteristic of certain conclusions drawn from statistical tests, it does not accurately describe the null hypothesis itself.