ss_best_normal: Sample Size for Selecting the Best Treatment in a Normal...

In ssutil: Sample Size Calculation Tools

Sample Size for Selecting the Best Treatment in a Normal Response (Indifference-Zone)

Description

Calculates the minimum common sample size per group needed to achieve a specified probability (power) of correctly selecting the best group using the indifference-zone approach. This method assumes normally distributed responses with a known and common standard deviation.

Usage

ss_best_normal(power, dif, sd, ngroups, seed = NULL)

Arguments

power	Numeric. Desired probability of correctly selecting the best group.
dif	Numeric. Indifferent-zone. Minimum difference that is considered meaningful.
sd	Numeric. Common standard deviation of the response variable.
ngroups	Integer. Number of groups (treatments) being compared.
seed	Optional. Integer seed to use in the internal call to multz().

Details

The indifference-zone approach guarantees that the probability of correct selection is at least power, assuming the best group's mean exceeds the others by at least dif. The calculation is based on Bechhofer's Procedure Nb.

Value

Integer. Sample size required per group to achieve the specified power.

Note

The function uses the quantile function multz(), which computes critical values for the selection procedure. This implementation assumes equal variances and independent samples.

References

Bechhofer, R.E., Santner, T.J., & Goldsman, D.M. (1995). Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons. Wiley Series in Probability and Statistics. ISBN: 0-471-57427-9.

Examples

ss_best_normal( power = 0.8, dif = 0.5, sd = 1, ngroups = 3)

ssutil documentation built on Sept. 18, 2026, 1:06 a.m.