The relationship between measurement reliability and statistical power is a complex one. Where reliability is deﬁned by classical test theory as the proportion of ‘true’ variance to total variance (the sum of true score and error variance), power is only functionally related to total variance. Therefore, to explore direct relationships between reliability and power, one must hold either true-score variance or error variance constant while varying the other. Here, visualisations are used to illustrate the reliability-power relationship under conditions of ﬁxed true-score variance and ﬁxed error variance. From these visualisations, conceptual distinctions between ﬁxing true-score or error variance can be raised. Namely, when true-score variance is ﬁxed, low reliability (and low power) suggests a true eﬀect may be hidden by error. Whereas, when error variance is ﬁxed, high reliability (and low power) may simply suggest a very small eﬀect. I raise several observations I hope will be useful in considering the utility of measurement reliability and it’s relationship to eﬀect sizes and statistical power.