Which error occurs when the null hypothesis is actually false, but the test fails to reject it?

Which error occurs when the null hypothesis is actually false, but the test fails to reject it?

• A. Type I error
• B. Type II error
• C. Regression toward the mean
• D. Power

Answer: (B)

In hypothesis testing, a Type II error happens when the null hypothesis is false but the test fails to reject it. In real terms, there is a true effect, but the study doesn’t detect it, so you miss it. This is different from a Type I error, which would mean concluding there is an effect when there actually isn’t one. Regression toward the mean is a separate statistical phenomenon, not an error type in hypothesis testing. Power is the test’s ability to detect a true effect; higher power means a lower chance of making a Type II error, and power improves with larger sample sizes or larger actual effects. For example, if a new drug truly lowers blood pressure but the study isn’t able to show significance, that’s a Type II error.

In hypothesis testing, a Type II error happens when the null hypothesis is false but the test fails to reject it. In real terms, there is a true effect, but the study doesn’t detect it, so you miss it. This is different from a Type I error, which would mean concluding there is an effect when there actually isn’t one. Regression toward the mean is a separate statistical phenomenon, not an error type in hypothesis testing. Power is the test’s ability to detect a true effect; higher power means a lower chance of making a Type II error, and power improves with larger sample sizes or larger actual effects. For example, if a new drug truly lowers blood pressure but the study isn’t able to show significance, that’s a Type II error.