Monitoring individual athlete performance requires more than comparing two test scores, it demands an understanding of whether observed changes exceed normal measurement error. In this practical coaching article, Anthony Turner explains a straightforward method for analysing performance changes in a single athlete (N = 1) using standard deviation (SD) and confidence intervals. The paper introduces key concepts including the coefficient of variation (CV), standard error of measurement (SEM) and standard deviation before demonstrating why SD provides a simple and effective threshold for identifying meaningful change in individual athletes. The article also explores the trade-off between Type I (false positive) and Type II (false negative) errors, encouraging coaches to adopt a philosophy that reflects the practical consequences of each within high-performance sport. Practical examples show how to interpret overlapping confidence intervals, establish realistic performance targets and evaluate whether athletes have genuinely improved, maintained performance or declined between testing sessions. The article concludes by emphasising that robust decision-making depends on balancing statistical confidence with coaching judgement while ensuring testing methods are reliable enough to detect meaningful performance adaptations.