Do you mean that control and treatment samples should have the same mean but 1:8 variances? I think you need a related but slightly more general distribution to do that: the Gamma. Gamma(alpha, beta) has mean=alpha*beta and variance=alpha*beta**2. As an introduction, here is how to generate an illustration of Gamma(1,1) and Gamma(0.125,8) (same mean, 1:8 variances) data gamma; do x = 0.1 to 10 by 0.1; y1 = PDF("GAMMA", x, 1, 1); y2 = PDF("GAMMA", x, 0.125, 8); output; end; run; proc sgplot data=gamma; series x=x y=y1; series x=x y=y2; run; Here is how to simulate data groups from those distributions and look at the resulting sample distributions: data test; call streaminit(875665); do clustNb = 20, 40; do clustNo = 1 to clustNb; do sampSize= 10, 20; group="Treatment"; do i = 1 to int(sampSize/2); x = 8 * RAND("GAMMA", 0.125); output; end; group="Control"; do i = int(sampSize/2)+1 to sampSize; x = 1 * RAND("GAMMA", 1); output; end; end; end; end; run; proc univariate data=test; class group; var x; histogram; run; PG
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