Hi,

when one wish to test if there is a significant difference between two samples, he/she can use t-test (parametric) or M-W test (non-parametric). In case of more than 2 samples, 1-way Anova (parametric) or KW (non-parametric).

Now, the choice between t-test and 1-way Anova, on the one hand, and M-W and K-W, on the other hand, lies on whether or not your data meet the assumptions of parametric tests. I assume that you should know the difference between

parametric vs

non-parametric tests.

So, provided the fact that I do not know your data, the only guess I can do is that given the sample size (1000 for each sample), you could use t-test (or 1-way ANOVA in case of more than 2 samples).

t-test (or 1-way Anova) will test if there is a significant difference in mean value between samples.

Should you data not meet the assumptions for parametric tests, you should switch to the non-parametric "version", like MW or (in case of more than 2 samples) KW. As for these tests, I refer you to this

earlier post.

So, in essence, the choice between parametric vs non-parametric depends on the features of your data:

1) inspect your data

2) see if they meet the assumptions for t-test (or 1-way Anova)

3) if they do, go on with parametric tests

4) if they do not, switch to non-parametric tests (MW or KW)

hope this helps

regards

Gm