A Type II error (beta error) in hypothesis testing is defined as:
- A Rejecting the null hypothesis when it is true
- B Failing to reject the null hypothesis when it is actually false ✓
- C Setting the significance level too low
- D Using a one-tailed test when two-tailed is appropriate
Correct answer: B. Failing to reject the null hypothesis when it is actually false
Explanation
Type II error (beta error, false negative) is the failure to reject the null hypothesis when it is actually false (i.e., failing to detect a true effect). Type I error (alpha error) is rejecting the null hypothesis when it is true. The power of a study (1-beta) represents the probability of correctly detecting a true effect. To minimize Type II error, sample size should be adequate and power should be at least 80%.
Reference: Park's Textbook of Preventive and Social Medicine, 27th ed.
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