A plot of the probability of finding a true effect (on the vertical axis) as a function of the difference between the true value measured and the hypothesized value (on the horizontal axis). As the difference between the actual and hypothesized values increases, the probability of accepting the hypothesis increases. As the efficiency of the test increases, the probability of accepting the hypothesis increases. Test efficiency is a function of choosing the best test, reducing required confidence, reducing variance, and increasing sample size. By convention, a priori sample sizes are estimated to produce a power of .80, given α = .05 and measure of effect and variance estimated from the literature. [See alpha, operating characteristic curve, power, Type II error