A variety of regression-based methods have been used to diagnose moderation effects, typically categorized into two broad approaches. The t-test-based approach includes moderated regression analysis and conditional process analysis, while the F-test-based approach includes hierarchical moderated regression and subgroup analysis. Although these methods rely on the same underlying moderated regression model, they often produce inconsistent conclusions due to differences in their statistical assumptions and inferential frameworks. Notably, prior research has not addressed how to interpret such inconsistencies or determine which method offers the most accurate diagnosis of moderation effects. This paper proposes a general regression-based analysis that evaluates the interaction effect without imposing any restrictions on the correlations among estimated model components. We show that previous methods are restricted versions of this general analysis, each operating under specific assumptions about parameter independence. Through theoretical exposition and a simulation study, we demonstrate how these assumptions affect standard errors, statistical conclusions, and ultimately the interpretation of moderation. The general analysis offers a unified framework for comparing alternative methods and for diagnosing moderation more precisely, particularly in the presence of multicollinearity.