A Type II error (beta error) in a clinical trial means:

• A Concluding there is a significant difference when none truly exists
• B Failing to detect a true difference — concluding no effect when one truly exists ✓
• C Calculating the p-value incorrectly due to small sample size
• D Applying a two-tailed test when a one-tailed test was indicated

Explanation

Type II (β) error = false negative — failing to reject the null hypothesis when it is actually false, i.e., concluding there is no effect when a true effect exists. It results in missed discoveries. Statistical power = 1 - β, the probability of correctly detecting a true effect. Type I (α) error = false positive — rejecting the null hypothesis when it is actually true. Increasing sample size reduces both α and β errors but has greater impact on β. Convention: α = 0.05; β = 0.20 (power 80%) is the minimum acceptable in RCTs.

Reference: Park's Textbook of Preventive and Social Medicine, 27th ed.

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