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jginestet
jginestet
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You have a sample with 20,000+ data points. That is a "huge" sample. Any test against a theoretical distribution, with such a large sample, will fail, no matter what the truth is. Which is why all your p-values are 0.000 (did you not find that odd?). See e.g. here for normality tests on CV, but this would apply to any similar Q-Q test (because real world data is never one of these perfect mathematical distributions).
Now, a possible sugestion? Take a much smaller sub-sample from your 20,000+ data points, say only 100-200 (so maybe every 100 or so observation?), or even take several such small sub-samples, and try again various theoretical distributions. I feel rather confident that several will give you large-ish p-values; then pick the one which had p-values above some threshold for all/most of the sub-samples.
Last, a comment; your sample is very "well-behaved"; symmetrical, uni-modal, centered on 0, and (very) large. For any analysis you may want to do neyondbeyond your question, you might just as well assume it is normal (which you did not test? but it would have failed as well).

You have a sample with 20,000+ data points. That is a "huge" sample. Any test against a theoretical distribution, with such a large sample, will fail, no matter what the truth is. Which is why all your p-values are 0.000 (did you not find that odd?). See e.g. here for normality tests on CV, but this would apply to any similar Q-Q test (because real world data is never one of these perfect mathematical distributions).
Now, a possible sugestion? Take a much smaller sub-sample from your 20,000+ data points, say only 100-200 (so maybe every 100 or so observation?), or even take several such small sub-samples, and try again various theoretical distributions. I feel rather confident that several will give you large-ish p-values; then pick the one which had p-values above some threshold for all/most of the sub-samples.
Last, a comment; your sample is very "well-behaved"; symmetrical, uni-modal, centered on 0, and (very) large. For any analysis you may want to do neyond your question, you might just as well assume it is normal (which you did not test? but it would have failed as well).

You have a sample with 20,000+ data points. That is a "huge" sample. Any test against a theoretical distribution, with such a large sample, will fail, no matter what the truth is. Which is why all your p-values are 0.000 (did you not find that odd?). See e.g. here for normality tests on CV, but this would apply to any similar Q-Q test (because real world data is never one of these perfect mathematical distributions).
Now, a possible sugestion? Take a much smaller sub-sample from your 20,000+ data points, say only 100-200 (so maybe every 100 or so observation?), or even take several such small sub-samples, and try again various theoretical distributions. I feel rather confident that several will give you large-ish p-values; then pick the one which had p-values above some threshold for all/most of the sub-samples.
Last, a comment; your sample is very "well-behaved"; symmetrical, uni-modal, centered on 0, and (very) large. For any analysis you may want to do beyond your question, you might just as well assume it is normal (which you did not test? but it would have failed as well).

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jginestet
jginestet
  • 5.3k
  • 1
  • 6
  • 19

You have a sample with 20,000+ data points. That is a "huge" sample. Any test against a theoretical distribution, with such a large sample, will fail, no matter what the truth is. Which is why all your p-values are 0.000 (did you not find that odd?). See e.g. here for normality tests on CV, but this would apply to any similar Q-Q test (because real world data is never one of these perfect mathematical distributions).
Now, a possible sugestion? Take a much smaller sub-sample from your 20,000+ data points, say only 100-200 (so maybe every 100 or so observation?), or even take several such small sub-samples, and try again various theoretical distributions. I feel rather confident that several will give you large-ish p-values; then pick the one which had p-values above some threshold for all/most of the sub-samples.
Last, a comment; your sample is very "well-behaved"; symmetrical, uni-modal, centered on 0, and (very) large. For any analysis you may want to do neyond your question, you might just as well assume it is normal (which you did not test? but it would have failed as well).