The P-value comparison decision rule constitutes a fundamental mechanism in frequentist statistical inference used to evaluate hypothesis significance relative to a pre-specified alpha level. This formal procedure involves comparing the calculated probability of observing test statistics as extreme or more extreme than those derived from sample data against a critical threshold, thereby determining whether to reject the null hypothesis within the domain of parametric and non-parametric testing frameworks. The rule operationalizes Type I error control by establishing a binary decision boundary that dictates statistical significance without reference to effect size magnitude or specific study contexts.
Prerequisite chain context: requires Null and Alternative Hypothesis Formulation in Statistical Testing.