Type II error is best described as which statement?

Type II error is best described as which statement?

• A. Occurs when we believe there is no effect in the population, when in fact there is.
• B. Occurs when we fail to reject a false null hypothesis.
• C. Occurs when the data are non-normally distributed.
• D. Occurs when the sample size is too small.

Answer: (B)

Type II error occurs when there is actually an effect in the population, but the test fails to detect it. In practice, this means the null hypothesis is false, yet we do not reject it at the chosen significance level. That precise situation is captured by the statement “fail to reject a false null hypothesis,” which is why it is the best answer. Think of power as the probability of avoiding this error: higher power means a greater chance of detecting a real effect. Power increases with larger sample size, clearer effects, and lower variability, up to the chosen alpha level. The other statements describe related ideas but not the core decision process: non-normally distributed data isn’t by itself a Type II error, and a small sample size is a factor that can raise the risk of Type II error rather than the definition itself.

Type II error occurs when there is actually an effect in the population, but the test fails to detect it. In practice, this means the null hypothesis is false, yet we do not reject it at the chosen significance level. That precise situation is captured by the statement “fail to reject a false null hypothesis,” which is why it is the best answer.

Think of power as the probability of avoiding this error: higher power means a greater chance of detecting a real effect. Power increases with larger sample size, clearer effects, and lower variability, up to the chosen alpha level. The other statements describe related ideas but not the core decision process: non-normally distributed data isn’t by itself a Type II error, and a small sample size is a factor that can raise the risk of Type II error rather than the definition itself.