What does it mean to have a high p value but a very very low effect size? What can I conclude? Is it necessary to report the effect size when p=0.998?

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    $\begingroup$ Have you constructed a confidence interval for the effect size? That may be helpful. $\endgroup$
    – wzbillings
    Oct 31, 2023 at 15:05
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    $\begingroup$ Why the "but" between "high p value" and "very very low effect size"?? Wouldn't a high p-value be expected to go with a low effect size? There's no hint of a contradiction that would suggest "but". $\endgroup$
    – Glen_b
    Oct 31, 2023 at 15:13
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    $\begingroup$ A high $p$-value is telling you the observations are close to those predicted by the null hypothesis, while a low $p$-value would suggest that the observations appear more extreme under that hypothesis. Seeing $99999$ heads when flipping a possibly biased coin $200000$ times ($p=0.9982$) does not guarantee the coin is unbiased, but does suggest that the extent of any bias is likely to be very small. $\endgroup$
    – Henry
    Oct 31, 2023 at 15:25
  • $\begingroup$ This page asks (and answers) essentially the same question: stats.stackexchange.com/questions/330713/… $\endgroup$ Oct 31, 2023 at 18:11

1 Answer 1


There is a strong case for reporting effect sizes alongside p-values, as they both represent two different things. The p-value indicates certainty/uncertainty, whereas the effect size tells you about the magnitude of the effect. You can read this article for example: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444174/

And refer to this post:


You could report both the effect size and p-value, saying that the difference between the two levels of your variable is small and non significant for example.


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