The core theoretical framework presented is the General Linear Model (GLM), which posits that all statistical inference within psychology and related disciplines can be reduced to a single unified mechanism: predicting an outcome variable from a model defined by parameters, subject to inherent error. This theory establishes that diverse analytical methods—from simple means of point estimation to complex regressions and ANOVA—are not distinct tests but rather variations of the same predictive equation where predictors are encoded differently or additional terms are added. The domain is undergraduate statistical education in psychology, specifically addressing the theoretical simplification needed to overcome student cynicism by demonstrating that understanding five abstract constructs—parameters, model fit (error), estimation methods, standard errors, and confidence intervals—is sufficient for mastering hypothesis testing and interpreting results without reliance on complex specific equations or historical test distinctions.
The core theoretical framework presented is the General Linear Model (GLM), which posits that all statistical inference within psychology and related disciplines can be reduced to a single unified me…