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I always like the inverse of this, which is that if you calculate that less than 68% is in the first standard deviation, you can guess that the data isn’t norma
by abakker 4y ago
I always like the inverse of this, which is that if you calculate that less than 68% is in the first standard deviation, you can guess that the data isn’t normally distributed.
It’s really a heuristic on where to start with analysis, though. Not a result in and of itself.
- qsort 4y agoThe gist of the idea is of course correct, but if you actually do this, please use an actual normality test. https://en.wikipedia.org/wiki/Shapiro–Wilk_test https://en.wikipedia.org/wiki/Shapiro–Wilk_test https://en.wikipedia.org/wiki/Anderson–Darling_test https://en.wikipedia.org/wiki/Anderson–Darling_test https://en.wikipedia.org/wiki/Kolmogorov–Smirnov_test https://en.wikipedia.org/wiki/Kolmogorov–Smirnov_test
- disgruntledphd2 4y agoThe trouble with normality tests is that you either have too little data (in which case they're useless) or too much (in which case their also useless). I always think of normality as a nice theory for simple stuff, but you want to be running sensitivity analyses for all of your assumptions, including normality (which I don't think I've ever seen professionally, tbh).