A Student t test assumes normally distributed data with equal variances.(adsbygoogle = window.adsbygoogle || []).push({});

I know you can test the Gaussian distribution with the Kolmogorov and Smirnov test and test the variances with the F-test.

When data is not normal you use a non-parametric test (Mann-Whitney test), when variances are significantly different you use the Welch-corrected t test.

How strict should I follow those rules?

According to this site (http://www.graphpad.com/articles/interpret/Analyzing_two_groups/choos_anal_comp_two.htm [Broken]) the rules work well for >100 samples and works poorly for <12 samples. How about the region in between?

I have samples sets ofnaround 20, some are not normally distributed. Can I go ahead and do a t test, or should I maybe log transform all the data before doing the t test? Or do a Mann-Whitney test?

Thanks for your input, here is a graph with the data distribution for the 4 samples, together with the 95% CI:

http://img301.imageshack.us/img301/9940/scatter95cifg4.jpg [Broken]

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# When not to use the Student t test?

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