Inter-limb asymmetry is often misunderstood in sports and healthcare, with natural differences seen as problems usually needing correction. Evidence linking inter-limb asymmetries to increased injury risk or reduced performance is weak, and asymmetries of 5–15% (or even higher) typically do not increase the likelihood of injury. Assessing inter-limb asymmetries is a complex matter. Practitioners should select tests aligned with sports demands and track changes over time, rather than relying on single time point data. Ongoing temporal assessments help distinguish meaningful trends from natural fluctuations. Measurement error should also be considered to ensure changes exceed the minimal detectable change and reflect genuine performance or shifts in injury risk. Intra-individual analysis is recommended over averages across groups, as they can obscure meaningful variations. Arbitrary thresholds for what may be considered “normal” asymmetries oversimplify a continuous variable, potentially leading to misleading conclusions. Focusing on ranges (e.g., confidence intervals) instead of point values (e.g., mean) provides a more nuanced view. In addition, interpreting raw limb data alongside asymmetry metrics is crucial, as similar asymmetry percentages may arise from different limb strength profiles. Tracking raw data ensures that interventions improve performance, even if asymmetries persist. We provide a framework to help guide practitioners’ decisions. Task specificity and context, temporal stability, measurement quality, and raw performance data are key pieces of the puzzle. Before implementing “asymmetry-correcting” programs, practitioners should answer key questions, for which we provide a user-friendly decision tree. Not all asymmetries are likely to yield meaningful benefits if corrected, and intervening in asymmetry should result from a carefully reasoned process that requires establishing relevance, ensuring measurement quality, gathering appropriate data, and considering practical implications.