What defines a type II error?

What defines a type II error?

• A. The null hypothesis is accepted when it is false
• B. The null hypothesis is rejected when it is true
• C. The sample size is too small
• D. The effect size (γ) is not defined in advance

Answer: (A)

A type II error occurs when the null hypothesis is accepted when in fact it is false. In the context of hypothesis testing, the null hypothesis typically represents a statement of no effect or no difference. A type II error reflects a failure to detect an effect or difference that truly exists, leading researchers to incorrectly conclude that there is insufficient evidence to reject the null hypothesis. In contrast, rejecting the null hypothesis when it is true is defined as a type I error. Sample size and effect size relate to the statistical power of a test, which can influence the probability of making a type II error but do not define it directly. Understanding type II errors is crucial for designing studies that balance the risks of incorrectly accepting the null hypothesis against the consequences of potential missed opportunities to detect true effects.

A type II error occurs when the null hypothesis is accepted when in fact it is false. In the context of hypothesis testing, the null hypothesis typically represents a statement of no effect or no difference. A type II error reflects a failure to detect an effect or difference that truly exists, leading researchers to incorrectly conclude that there is insufficient evidence to reject the null hypothesis.

In contrast, rejecting the null hypothesis when it is true is defined as a type I error. Sample size and effect size relate to the statistical power of a test, which can influence the probability of making a type II error but do not define it directly. Understanding type II errors is crucial for designing studies that balance the risks of incorrectly accepting the null hypothesis against the consequences of potential missed opportunities to detect true effects.