In scientific measurement and data analysis, the concepts of accuracy and precision describe two distinct metrics used to evaluate the quality of results. Accuracy refers to the proximity of a measured value to the true or accepted theoretical value; a measurement is highly accurate if it consistently hits the target value. Conversely, precision relates to the reproducibility of the measurements, indicating how closely grouped repeated measurements are to each other. A highly precise set of data points will cluster tightly together, even if that cluster is far from the true value. It is possible, for example, to take measurements that are extremely precise (close to each other) but fundamentally inaccurate (far from the true value). Optimal scientific measurements aim to achieve both high accuracy and high precision, suggesting a robust methodological approach is necessary to minimize systematic and random errors.