Differentiating between statistical significance and practical significance—commonly referred to as clinical relevance, meaningful difference, or minimal clinically important/important difference (MCID/MID)—is crucial for sound evaluation within a scientific framework. While p-values provide a probabilistic assessment of data within the scope of the null hypothesis, they remain silent on the matter of whether the associated effect holds any significance in real-world scenarios. Some researchers critique the prevalent misuse of p-values, arguing that practical or clinical significance is often overshadowed by the focus given to statistical significance. Such misinterpretation is bound to result in erroneous choices and actions in health, policy, or business. Instead, Bayesian statistics are offered as a remedy, albeit they do not resolve the issue of defining practical relevance using MCID/MID thresholds. Statistical significance, specifically the p-value threshold of p < 0.05, has received critique from various corners for being arbitrary. With sufficiently large sample sizes, even the most trivial of effects will be deemed to be statistically significant. Hence, to avoid this theoretical trap, effect sizes and confidence intervals, along with relevant domain expertise and patient-reported outcomes (PROs), need to be integrated. Clinical relevance goes a step further and ascertains whether the treatment effect is perceivable, justifiable, and whether the benefits outweigh the costs of intervention. MCID, which applies primarily to health care, also informs other domains such as linguistics and biology. It is crucial to note that MCID is not ubiquitous. The chapter demonstrates how employing different approaches contributes to considerable variability in MCID estimates, underscoring the need for customized or stratified methods. In the end, the chapter strongly advocates for the blend of MCID/MID with what is called “practical relevance” to support statistical outcomes that are not only significant but also meaningful and useful in real-world applications.