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It looks like R's cor.test returns p-values of exactly zero if the real p-value is very low. For example:

> sprintf('%e', cor.test(1:100, c(1.2, 1:98, 1.1), method='spearman')$p.value)
[1] "0.000000e+00"

In SciPy this same test results in a very low, but nonzero, p-value:

> print scipy.stats.spearmanr(range(100), [1.2]+range(98)+[1.1])
(0.94289828982898294, 1.3806191275561446e-48)

Presumably the p-value gets rounded down to 0 if the value becomes so small that R cannot represent it anymore using its normal floating point type? Is there a simple way to obtain the exact number or is the best I can do to report p < 2.2e-16?

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closed as off-topic by kjetil b halvorsen, Ferdi, Peter Flom Sep 27 '18 at 11:52

This question appears to be off-topic. The users who voted to close gave this specific reason:

  • "This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the support links we maintain." – kjetil b halvorsen, Ferdi, Peter Flom
If this question can be reworded to fit the rules in the help center, please edit the question.

  • 1
    $\begingroup$ See the help page for cor.test where it states "pKendall and pSpearman in package SuppDists, spearman.test in package pspearman, which supply different (and often more accurate) approximations." Loading SuppDists, extracting $estimate from your cor.test result, and passing it to pSpearman gives an allegedly exact value. $\endgroup$ – whuber Jun 29 '15 at 22:20
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    $\begingroup$ Tests in vanilla R simply don't report p-values lower than .Machine$double.eps. See that link for an explanation of why it makes little sense to discuss any notion of "exact" p values anywhere near that small anyway. It's a bit like arguing about how many angels can dance on the head of a pin, when you can only look at a different type of pin to the kind you want to discuss. The extreme tails depend heavily on assumptions like between-point independence. $\endgroup$ – Glen_b Jun 30 '15 at 1:09
  • $\begingroup$ Ben Bolker's comment under another answer at that link is especially apt: "such small p-values [...] are so tiny that the probability that the NSA broke in and tampered with your data [...] is far, far, higher than the nominal p-value. ". (I sometimes give examples that relate to cosmic rays flipping a few important bits in your data, but I think his example is probably more apt.) $\endgroup$ – Glen_b Jun 30 '15 at 1:21
  • $\begingroup$ @Glen_b It is nevertheless worthwhile paying attention to such things. Tiny differences among unusual values can sometimes be the only evident indications that something is wrong with an algorithm: they are the proverbial canaries. In this case it is also intriguing that SciPy's Spearman $\rho$ statistic (as well as its p-value) differs from that in R, even though they agree to many d.p. Although these differences may be inconsequential, upon observing them we must immediately mistrust the output of both programs for all inputs until we understand the reason for the discrepancy. $\endgroup$ – whuber Jun 30 '15 at 13:23
  • $\begingroup$ @whuber certainly the difference in $\rho$ values is important; with that, we'd expect a difference in p-values. I simply didn't address that aspect in my comments. $\endgroup$ – Glen_b Jun 30 '15 at 16:10