I'm a PhD student in the field of Bioengineering (sorry in advance for my poor english, btw). I'm analyzing some data to be included in a paper and have some concerns about what'd be the proper test for me to use. Basically, I'm studying the effect of increasing the concentration of a given reagent on the time the chemical reaction takes to start (from now on, latency time). When plotting the data (mean +/- SD), I see two particular consecutive concentrations (C1 and C2; N=9) with slightly different means and with SD that make the values overlap partially in the plot. With the naked eye, one would say both "populations" are not significantly different because of the overlapping. Generally, I like Kolmogorov-Smirnov test since it takes into account all possible differences among groups, not just the mean. By unpaired two-samples K-S test, the two populations I mentioned were not significantly different, but then I decided to perform an ANOVA since everybody does it (most of the cases without taking into account its assumptions, though)... and guess what: both groups were significant (and the same happened when performing t-test).

Then the question arised: should I trust K-S or ANOVA/t-test? For me it'd be odd to say two groups that overlap in terms of mean+/-SD are significantly different, but maybe it's just a concept problem of mine.

Fow what I've read, unpaired two-samples K-S is a pretty potent test for small populations and I thought it'd be a good alternative given the heteroschedasticity I found in my data. But I've read also it's not a decent test, so I don't know what to trust.

I'd really appreciate your help since my mates and bosses seem not to find consensus. Most of them agree with "seeing the groups as populations (mean+/-SD)" but, at the end, systematically run ANOVA tests.

Thanks in advance.

Aitor.