Bayesian statistics differs from frequentist statistics primarily in that:
- A Bayesian methods use larger sample sizes and are therefore more powerful
- B Bayesian analysis incorporates prior probability (prior belief) and updates it with observed data to yield a posterior probability ✓
- C Bayesian methods use non-parametric tests and do not require normal distribution assumptions
- D Frequentist statistics allows probability statements about hypotheses while Bayesian statistics does not
Correct answer: B. Bayesian analysis incorporates prior probability (prior belief) and updates it with observed data to yield a posterior probability
Explanation
Bayesian statistics formally incorporates prior knowledge (prior probability distribution) and updates it using observed data via Bayes' theorem to produce a posterior probability distribution. This allows direct probabilistic statements like 'there is a 95% probability that the true effect lies within this interval' (credible interval), unlike the frequentist confidence interval which is a statement about repeated sampling procedures. Frequentist p values do not represent the probability that a hypothesis is true.
Reference: Park's Textbook of Preventive and Social Medicine, 27th ed.
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