I am using mt.teststat to generate t-statistics assuming equal and then unequal variances (to check some results from another calculation). Regardless of whether I specify test="t" or test="t.equalvar" I get ther same result from mt.test (I believe it is assuming equal variances for either option). Below is the code I used, even checking to make sure the variances weren't actually equal. #RMA.AB is an RMA Expression Set with 12626 genes and 6 samples. The first three columns represent treatment A, the last three columns represent treatment B. > dim(exprs(RMA.AB)) [1] 12626 6 > cl<-c(rep(1,3),rep(0,3)) > teststat1<-mt.teststat(exprs(RMA.AB),cl,test="t") # should return test statistics based on two-sample Welch t-statistics (unequal variances) > teststat1[1:10] [1] 2.4102276 1.3967185 1.2955078 1.3675017 1.0794415 [6] 2.1334274 1.1076244 -0.2068253 1.0834689 1.9387467 > teststat2<- mt.teststat(exprs(RMA.AB),cl,test="t.equalvar") # should return test statistics based on two-sample t-statistics with equal variance for the two samples > teststat2[1:10] [1] 2.4102276 1.3967185 1.2955078 1.3675017 1.0794415 [6] 2.1334274 1.1076244 -0.2068253 1.0834689 1.9387467 > sum(teststat1-teststat2) [1] 5.551115e-17 # teststat1=teststat2 #Check var(RMA.A) versus var(RMA.B) > RMA.A<-exprs(RMA.AB)[,1:3] > RMA.B<-exprs(RMA.AB)[,4:6] > var.A<-apply(RMA.A,1,var) > var.B<-apply(RMA.B,1,var) > var.A[1:10] 100_g_at 1000_at 1001_at 1002_f_at 1003_s_at 0.022248140 0.009201980 0.019508012 0.008462727 0.003681282 1004_at 1005_at 1006_at 1007_s_at 1008_f_at 0.018467891 0.016931126 0.010809741 0.018222501 0.026152638 > var.B[1:10] 100_g_at 1000_at 1001_at 1002_f_at 1003_s_at 0.017586464 0.010112982 0.059191673 0.030372182 0.023888130 1004_at 1005_at 1006_at 1007_s_at 1008_f_at 0.014180197 0.014445595 0.009923636 0.012814546 0.008584633 #So, var(RMA.A) is not equal to var(RMA.B) Why are they returning the same result and how can I get the t-statistic assuming UNEQUAL variances? Thanks. Ann