Biostatistics & Research · single question
A Type II error (beta error) in a study means:
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Correct answer: A. Concluding there is no difference when one actually exists
A Type II error (β error) means the study fails to detect a real difference that exists — concluding 'no significant difference' when there actually is one. It is the error of 'missing' a true effect (false negative result in hypothesis testing). Type I error (α) is the opposite: concluding there IS a difference when there is none (false positive). Power (1−β) is the probability of correctly detecting a real difference.
- A. ✅ CORRECT – Type II error: failing to reject the null hypothesis when it is actually false — missing a real difference.
- B. ❌ Sample size affects the probability of Type II errors (small samples → higher Type II error risk) but this is not the definition of a Type II error.
- C. ❌ p<0.05 indicates statistical significance — it is not related to the definition of Type II error.
- D. ❌ Concluding there is a difference when there is none = Type I error (α error, false positive).
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