A researcher tests whether a new drug reduces SBP more than placebo. The null hypothesis is H₀: mean SBP difference = 0. The study finds p = 0.03. If the true treatment difference is zero (drug has no real effect), this result represents:

• A A Type II error (false negative)
• B Correct rejection of null hypothesis (power)
• C A p-value too small to be meaningful
• D A Type I error (false positive) ✓

Explanation

A Type I error (alpha error) occurs when the null hypothesis is true but is incorrectly rejected. Here, if the drug truly has no effect (H₀ is true) but the study finds p = 0.03 (rejecting H₀), this is a false positive—a Type I error. The probability of this error equals alpha (typically set at 0.05). Type II error (beta error) is failing to reject a false null hypothesis. The p-value of 0.03 alone does not make the result 'correct'; it simply means the result would occur by chance 3% of the time if H₀ were true.

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

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