![]() ![]() Beta: The probability of a type-II error - not detecting a difference when one actually exists.Most medical literature uses an alpha cut-off of 5% (0.05) - indicating a 5% chance that a significant difference is actually due to chance and is not a true difference. Alpha: The probability of a type-I error - finding a difference when a difference does not exist.Treatment Effect Size: If the difference between two treatments is small, more patients will be required to detect a difference.Population Variance: The higher the variance (standard deviation), the more patients are needed to demonstrate a difference.Baseline Incidence: If an outcome occurs infrequently, many more patients are needed in order to detect a difference.Generally speaking, statistical power is determined by the following variables: Enrolling too many patients can be unnecessarily costly or time-consuming. By enrolling too few subjects, a study may not have enough statistical power to detect a difference (type II error). 1īefore a study is conducted, investigators need to determine how many subjects should be included. This calculator uses a number of different equations to determine the minimum number of subjects that need to be enrolled in a study in order to have sufficient statistical power to detect a treatment effect. K = ratio of sample size for group #2 to group #1 About This Calculator Β = probability of type II error (usually 0.2) Α = probability of type I error (usually 0.05) Δ = |p 2-p 1| = absolute difference between two proportions P 1, p 2 = proportion (incidence) of groups #1 and #2 ![]()
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