Between-day variance is an ambiguous term representing either total variance or pure between-day variance. In either case, it is often incorrectly calculated even though analysis of variance (ANOVA) and other excellent methods of estimation are available. We used statistical theory to predict the magnitude of error expected from using several intuitive approaches to estimation of variance components. We also evaluated the impact of estimating the total population variance instead of pure between-day variance and the impact of using biased estimators. We found that estimates of variance components could be systematically biased by several hundred percent. On the basis of these results, we make recommendations to remove these biases and to standardize precision estimates.