This chapter reviews several data-analytic strategies used in clinical assessment research. The goal of the chapter is to help the clinician critically evaluate published assessment research in order to select the best available assessment instruments and measures for an assessment context. The chapter covers the impact on clinical judgments of statistical significance and effect size in clinical research, along with common effect size indicators such as Cohen’s d and the Pearson correlation. The chapter also considers factors that contribute to true and error variance in measures, convergent and discriminant validity evidence, and data analytic strategies for gathering that evidence (including analysis of variance and covariance, regression, multiple regression, and logistic regression). When evaluating clinical assessment research, the clinician should consider the assumptions underlying the data analytic strategies used. One should evaluate not just whether a significant effect is present but also the magnitude of the effect. Moreover, effect sizes should be interpreted within the context of the research design and criterion variable upon which effect size estimates are based. The chapter ends with recommendations for interpreting the results of data analytic strategies used in psychometric evaluations.