The determination of the minimal clinically important difference (MCID) and minimal important difference (MID) is essential for evaluating the significance of changes in patient-reported outcomes (PROs) in clinical research and practice. These thresholds help distinguish between statistically significant results and those that are, in reality, meaningful and noticeable/perceivable to patients. This chapter introduces the three main methodological approaches to determining MCID: distribution-based methods, anchor-based methods, and hybrid methods that combine both. Distribution-based methods rely solely on statistical properties of the data. Key techniques include the standard error of measurement (SEM), effect size (ES), and minimal detectable change (MDC). SEM quantifies how much variation in scores is due to measurement error and helps identify real changes in individual patient scores. MDC, derived from SEM, establishes a threshold beyond which a change is unlikely to be due to random variability. Effect size, often measured with standardized differences such as Cohen’s d, provides the magnitude of change, which is categorized as small, medium, or large. These methods, albeit robust, lack clinical context and other demographic and clinical variables. Hence, they must be interpreted with caution. Anchor-based methods, in contrast, rely on external criteria—such as patient, caregiver, or clinician ratings—to determine what constitutes a meaningful and noticeable change. For example, a patient might rate their current health compared to an earlier point using a global rating of change scale. This subjective input is crucial in capturing what patients perceive as improvements or deteriorations in their condition. Clinician evaluations also serve as valid anchors, especially when determining the relevance of changes across various domains of functioning. Hybrid methods combine the strengths of both distribution- and anchor-based approaches. This allows researchers to triangulate findings and produce more comprehensive and reliable MCID estimates. By integrating statistical evidence with patient and clinician perspectives, hybrid methods increase the validity and applicability of the results. Advanced statistical models such as multivariate regression, linear mixed effects models (LMEM), and generalized additive mixed models (GAMM) enhance the estimation of MCID, especially in longitudinal studies. These models account for repeated measures and interindividual variability, making them ideal for tracking patient progress over time. Techniques like structural equation modeling (SEM) also allow researchers to analyze complex relationships between latent constructs and observed outcomes. Meta-analytic techniques further refine MCID estimates by synthesizing data across studies. Pooling standard deviations and PROs from various studies ensures broader generalizability. However, heterogeneity in patient populations, measurement instruments, and methods can pose challenges. Ultimately, determining MCID is not a one-size-fits-all process. It varies by patient characteristics, conditions, and instruments used. A thoughtful combination of methodologies ensures that the MCID reflects both statistical rigor and clinical relevance, fostering patient-centered care and more effective clinical decision-making.