One of the most common questions asked within school psychology research concerns the relationship between an intervention (independent variable) and an outcome (dependent variable). For example, say a researcher is interested in the effect of a cognitive-behavioral intervention (independent variable) on student depression (dependent variable). They evaluate this relationship by comparing depression levels across three groups, including those who received the intervention in a one-on-one format, those who received it in a small-group format, and those who received "treatment-as-usual" with no counseling. A second researcher wants to determine whether a Tier 2 small-group reading intervention (independent variable) demonstrates a positive effect on student reading comprehension (dependent variable). They evaluate this relationship with a single group of students by comparing preintervention comprehension levels to postintervention comprehension levels. For several decades, the most common approach to evaluating these causal questions has been through analysis of variance (ANOVA). ANOVA represents a family of analyses that might be used to determine if mean scores on some dependent variable differ across multiple time points and/or multiple groups. Different ANOVA-related statistics can be calculated in evaluating the extent of this difference in terms of both statistical and practical significance. This article gives a brief description of six common ANOVA models, a summary of those models, and the guidelines to critically evaluate ANOVA findings.