This article introduces the concept of the carrying capacity of data (CCD), defined as an integrated, evaluative judgment of the credibility of specific data-based inferences, informed by quantitative and qualitative analyses, leavened by experience. The sequential process of evaluating the CCD is represented schematically by a framework that can guide data analysis and statistical inference, as well as pedagogy. Aspects of each phase are illustrated with examples. A key initial activity in empirical work is data scrutiny, comprising consideration of data provenance and characteristics, as well as data limitations in light of the context and purpose of the study.  Relevant auxiliary information can contribute to evaluating the CCD, as can sensitivity analyses conducted at the modeling stage. It is argued that early courses in statistical methods, and the textbooks they rely on, typically give little emphasis to, or omit entirely, discussion of the importance of data scrutiny in scientific research. This inattention and lack of guided, practical experience leaves students unprepared for the real world of empirical studies. Instructors should both cultivate in their students a true respect for data and engage them in authentic empirical research involving real data, rather than the context-free data to which they are usually exposed.

Although regression to the mean is pervasive in data analysis, educational researchers often misconstrue it as evidence of genuine change and mistakenly attribute random changes to treatment effects. A statistical phenomenon where extreme values naturally move closer to the average after repeated treatment, regression to the mean is especially susceptible to misinterpretations in educational studies with pretest-posttest or longitudinal designs. In such studies, observed changes are frequently assumed to be the effects of treatment, even in cases where the changes are statistical artifacts. Using a hypothetical case and two real-world studies, this paper investigates the technical challenges that regression to the mean poses and introduces a hybrid Bayesian model that mitigates its effects more effectively than conventional approaches, such as multiple baseline adjustments and formulaic corrections. In particular, the hybrid Bayesian model relies on multiple baseline measurements to minimize distortions associated with regression to the mean during the pretest phase and leverages prior knowledge—such as standard deviations and population means—to refine post-test data adjustments. It follows that the model provides educational researchers with an innovative tool for accurately evaluating interventions and enhancing the effectiveness of various research-driven educational policies and practices.