Which statement about the Null Hypothesis (Ho) is accurate in experimental testing?

Which statement about the Null Hypothesis (Ho) is accurate in experimental testing?

• A. The null hypothesis states that there is a change, difference, or relationship in the population.
• B. The null hypothesis is always the alternative.
• C. The null hypothesis predicts that the IV has no effect on the DV in the population.
• D. The null hypothesis is not used in statistical testing.

Answer: (C)

The key idea here is that the null hypothesis represents no effect, no difference, or no relationship between the manipulated variable and the outcome in the population. The statement that fits this best is that the null predicts the independent variable has no effect on the dependent variable in the population. In practice, any observed difference is considered to arise from random sampling variation unless the data provide strong evidence otherwise. This contrasts with the alternatives: asserting a change or relationship describes what the data would support if there is an effect, not the null. Saying the null is the alternative mixes up two opposing ideas, and claiming the null isn’t used in statistical testing ignores the whole framework of significance testing. When testing, you assess whether the observed results would be improbable if the null were true; if they are unlikely enough, you reject the null and infer evidence of an effect, otherwise you do not reject the null.

The key idea here is that the null hypothesis represents no effect, no difference, or no relationship between the manipulated variable and the outcome in the population. The statement that fits this best is that the null predicts the independent variable has no effect on the dependent variable in the population. In practice, any observed difference is considered to arise from random sampling variation unless the data provide strong evidence otherwise.

This contrasts with the alternatives: asserting a change or relationship describes what the data would support if there is an effect, not the null. Saying the null is the alternative mixes up two opposing ideas, and claiming the null isn’t used in statistical testing ignores the whole framework of significance testing. When testing, you assess whether the observed results would be improbable if the null were true; if they are unlikely enough, you reject the null and infer evidence of an effect, otherwise you do not reject the null.