Research and Scholarly Methods: Testing the Null Hypothesis

Null hypothesis significance testing (NHST) is the predominant statistical framework in biomedical research and clinical pharmacy, so maintaining a thorough understanding of NHST and its component parts is essential for practitioners. NHST begins with an assumption the null hypothesis is true, allowing researchers to test for differences while guarding against bias. Type I error (false positive) and type II error (false negative) are the possible misclassifications. Interpretation of the p-value depends on the assumption that the null hypothesis is true, rather than indicating the probability that the null hypothesis is correct. Misinterpretation of this value remains one of the most persistent errors in medical literature. Sound application of NHST requires deliberate study design, prospective specification of outcomes, and safeguards against multiplicity. Interpretation demands attention to more than statistical thresholds alone, emphasizing effect size, clinical significance, biological plausibility, and study quality. Responsible interpretation also requires distinguishing statistical inference from broader scientific inference, which incorporates prior knowledge and theoretical rationale. Practical considerations and illustrative cases highlight common pitfalls and strategies for improvement. In addition, emerging approaches, including reinterpretation of confidence intervals as compatibility intervals, use of false positive risk, and Bayesian analysis offer potential refinements, though controversies regarding their application remain. Understanding NHST as one component of scientific inference, rather than a definitive arbiter of truth, promotes more thoughtful application and interpretation of evidence. Careful statistical reasoning enhances the quality of research, the reliability of peer review, and the translation of findings into patient care.