There are a number of different tests for outliers (see below). Tukey's fence, used with the box plot, is very sensitive and will flag many

*potential outliers*. The larger your sample size, the more it will flag. I recommend using a more selective test from the ones below. Outliers should only be removed if you can find a reason such as a measurement error or transposing numbers. Otherwise, you should use methods that are robust to outliers.

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Hi Miner,

Nice table! but probably also confusing one as there are too many options

I assume usually using the 2 standard deviation method or Tukey fence with k=1.5 will do the job.

If I understand correctly, Rachy also runs the 2 standard deviation method for outliers and has 23 outliers, while if it was a normal distribution without outliers you would expect to get ~12 outliers.

So if the distribution and the outliers are reasonably symmetrical, probably ANOVA should still be OK, I assume there is no final agreement what is too many outliers ...

But of course, it will be safe also to run a non-parametric test, and since the test power should be strong (of course it depends on the required effect size), even a bit weaker non-parametric test should be strong enough.

A smaller significance level is probably a good idea, as suggested by katxt

Anyway Rachy, you should also look at the effect size.