The core theoretical mechanism is Central Limit Theorem (CLT) application within inferential statistics, where independent random samples drawn from any population distribution converge toward a normal sampling distribution for the sample mean as sample size increases. This principle formally underpins hypothesis testing frameworks, defining Type I error probabilities via p-values and establishing confidence intervals to quantify parameter uncertainty without reliance on manual calculation of specific formulas. The concept belongs to quantitative social science methodology, serving as the mathematical foundation that justifies parametric statistical tests like t-tests and ANOVA in non-experimental sociological research domains.
The core theoretical mechanism is Central Limit Theorem (CLT) application within inferential statistics, where independent random samples drawn from any population distribution converge toward a norm…