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  • $\begingroup$ In fact, t-test is a quiet robust test for non normal data. Inferences have to be taken with cautious in this case but you always have bootstrap. $\endgroup$
    – Mangnier Loïc
    Commented Apr 29 at 12:41
  • $\begingroup$ Maybe you could elaborate a bit more on what you did and what you expected to happen. For instance, in your code you seem to compare p-values, but that really only makes sense when you simulate a lot of samples. Then you talk about the t-test being "closer to the truth" and I think there might be multiple things you could mean by that, also things quite far apart from p-values. $\endgroup$
    – Finn Lübber
    Commented Apr 29 at 14:38
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    $\begingroup$ The meaning of "robust ... for a specified distribution" is unclear, because it seems to merge two distinct concepts: robustness refers to performance when there are departures from distributional assumptions whereas referring to a distribution seems to be asking about the power of a test. Could you please clarify what you mean? $\endgroup$
    – whuber ♦
    Commented Apr 29 at 14:49