Statistical Significance Reconsidered: The Role of Bayesian Methods in the Life Sciences
DOI:
https://doi.org/10.18192/osurj.v5i1.7653Abstract
Statistical reasoning underpins the interpretation of experimental results in the life sciences. For decades, frequentist hypothesis testing has dominated research practice, with the p-value serving as a heuristic benchmark of significance. Yet, p-values are often misunderstood and limited in what they reveal. Statistical power further complicates inference, as underpowered studies risk generating misleading results while overpowered designs may detect biologically trivial effects.
Bayesian methods provide an alternative framework that addresses several of these limitations. By incorporating prior knowledge and directly comparing the probabilities of competing hypotheses, Bayesian inference produces richer outputs, such as Bayes factors, posterior probabilities, and credible intervals. Posterior predictive checks allow researchers to evaluate model fit and simulate new data, a valuable tool when replication is costly. However, Bayesian statistics also introduces challenges: the choice of prior can bias results, and computational demands can be steep for complex models.
This review synthesizes the strengths and weaknesses of both frequentist and Bayesian approaches, emphasizing their philosophical differences and practical implications for experimental design, ethics, and interpretation in the life sciences. Rather than positioning one framework as superior, we argue for statistical “bilingualism,” where fluency in both traditions equips researchers to balance standardized inference with adaptive, knowledge-rich modeling. Such dual competency is increasingly essential in an era of high-dimensional data, complex biology, and growing demands for transparency in scientific practice.
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