In hypothesis testing, the decision rule 'reject H0 if the p-value is less than alpha' describes which concept?

In hypothesis testing, the decision rule 'reject H0 if the p-value is less than alpha' describes which concept?

• A. Degrees of freedom
• B. p-value rule
• C. Confidence interval
• D. Type I error

Answer: (B)

This rule is about using the p-value to decide whether the observed data provide enough evidence against the null hypothesis. The p-value is the probability, assuming the null is true, of obtaining results as extreme or more extreme than what was actually observed. The significance level alpha is set before the test to limit how often we’d wrongly reject a true null (the false-positive rate). When the p-value falls below alpha, the observed data are unlikely under the null, so we reject H0. That directly links the strength of the evidence (how small the p-value is) to a formal decision rule. The other concepts are related but describe different ideas: degrees of freedom relate to the shape of the sampling distribution and critical values, a confidence interval shows a range of plausible parameter values and whether the null value lies inside, and Type I error is the probability of rejecting a true null, not the decision rule itself.

This rule is about using the p-value to decide whether the observed data provide enough evidence against the null hypothesis. The p-value is the probability, assuming the null is true, of obtaining results as extreme or more extreme than what was actually observed. The significance level alpha is set before the test to limit how often we’d wrongly reject a true null (the false-positive rate). When the p-value falls below alpha, the observed data are unlikely under the null, so we reject H0. That directly links the strength of the evidence (how small the p-value is) to a formal decision rule. The other concepts are related but describe different ideas: degrees of freedom relate to the shape of the sampling distribution and critical values, a confidence interval shows a range of plausible parameter values and whether the null value lies inside, and Type I error is the probability of rejecting a true null, not the decision rule itself.