Abstract

Statistical reasoning is fundamental to psychiatric practice, yet its application remains inconsistent and often misunderstood. This review aimed to examine the philosophical foundations and clinical applications of statistical reasoning in psychiatry, with particular attention to biases arising from their neglect. A comprehensive review and synthesis of literature was conducted from three domains, including philosophical foundations of probability theory, statistical methods in psychiatric research, and clinical applications of statistical reasoning. Common cognitive biases were analyzed in psychiatric decision-making, and strategies were identified for improving statistical reasoning in clinical practice. Statistical reasoning in psychiatry has evolved from Enlightenment empiricism through probability theory development to modern Bayesian and frequentist approaches. In clinical practice, statistical reasoning manifests in diagnostic decision-making (through Bayesian updating and test interpretation), treatment selection (through evidence evaluation and personalization), and prognostication (through risk assessment and communication). Neglecting statistical reasoning leads to systematic biases, including anchoring, confirmation bias, base rate neglect, and misinterpretation of statistical information, resulting in diagnostic errors and suboptimal treatment decisions. Integrating statistical reasoning into psychiatric practice requires balancing nomothetic (general) and idiographic (individual) approaches to knowledge. Educational reforms, structured decision support, and cultural shifts in psychiatric practice can help mitigate biases and improve clinical outcomes. Statistical reasoning should complement rather than replace clinical expertise, providing a framework for combining empirical evidence with individual patient characteristics and preferences.

Key words: Statistical reasoning, clinical decision-making, Bayesian reasoning, evidence-based psychiatry, review