# non parametric or parametric test for means of groups?

I have a series of experiments that are done in a series of blocks(groups). When comparing the means of group 1 and group 2 that arent statistically significant p=0.84. However looking at the group 1 mean and group 2 mean, group 1 is always lower. Can I take the mean of group1a, group1b, group1c, group1d and the mean of group2a, group2b, group2c, group2d and do a t test with that?

in R: example data

group1<-c(0.7142857, 0.7042857, 0.7160, 0.7142857)
group2<-c(0.7380952, 0.7480952, 0.7280952, 0.734)
t.test(group1, group2)


Welch Two Sample t-test

data: group1 and group2 t = -4.9851, df = 5.082, p-value = 0.003974 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.03761274 -0.01210151 sample estimates: mean of x mean of y 0.7122143 0.7370714

Are a, b, c, and d different experiments conducted on the same individuals (so that each individual takes tests a, b, c and d)? In that case, I don't think you should pool the results like you did.

Provided that the dependent variable is continuous, you could try a linear mixed model approach with group as a fixed effect and subject id as a random effect. This is needed to take into account that scores on the different tests might be correlated within each subject:

library(lme4)
lmer (score ~ group + (1|id))


If the experiments are scored differently, you should center and scale the scores (so that means and standard deviations are the same for all tests) before you enter them into the model.

• Hi Jonas, a,b,c,d are different experiments (same experiment different days) on the same individuals. The difference between Group1 and Group2 is I know something different about them and want to see if that difference is important. However, when I pool a,b,c,d group1 and do the same for group 2 and do a t test, I get nonsign data, but as mentioned group 1 mean is always lower than group 2 mean for each block comparison.