The chapter explains the concepts of minimal clinically important difference (MCID) and minimal important difference (MID), focusing on their relevance in differentiating between meaningful outcomes and mere statistical results. The book opens with a case study of India’s air pollution. These examples illustrate the overreliance on p-values and statistical thresholds—ignoring whether the change is meaningful to the patient or public health. The hypothetical study used to illustrate the concept assumes a statistically significant reduction in hospitalization due to asthma because of an odd–even vehicle usage scheme. The chapter comments on how clinical relevance is forgotten in the drive to prove something “significant.” Even with a minimal reduction of 0.5%, with a sufficiently large sample size, asthma hospitalizations could statistically “drop” during the odd–even scheme. However, this reduction would be imperceptible to the patients and irrelevant clinically and economically. The chapter then provides a conceptual and historical synopsis of MCID and MID. Both MCID and MID emerged in the clinical literature in the 1980s. MCID sought to define the minimum clinically relevant change in treatment effect that a patient perceives as beneficial or harmful enough to warrant a change in management. Unlike the probability threshold of p < 0.05, MCID employs relevance, subjectivity, and tangible outcomes. Moreover, it sets itself apart from economic significance, which concerns itself with whether the effect size justifies the costs incurred in the context of health technology assessment (HTA). To conclude, this chapter lays the groundwork for a book that intends to reposition MCID/MID from its clinical roots to include the more theoretical realms of psychology, economics, social science, and even the humanities. It advocates a change in one’s thinking from the shallow obsession with p-values to a thoughtful shift toward meaningful change that is anchored in PROs, clinical insights, and repercussions beyond the clinical setting. The subsequent chapters will discuss multidisciplinary MCID/MID computation and interpretation methods, practical issues, and conceptual frameworks.