Solved – Understanding the significance of very small p-values

I have calculated p-values of two independent groups (each group contains 17 samples and each sample is 100 points). I used scipy's mann whitney u test. Further, I combined the p-values using Fisher's method as suggested by one of my colleagues. However, the p-values are just too small to make any sense to me. Here is a sample output:

import scipy.stats as stats p_value_list = [3.3629559156133476e-56, 1.966307600030748e-254, 6.3484089727582271e-103,                  3.1221165197874102e-92, 5.8797795864262639e-128, 4.0807369798923832e-130,                  1.205918004734663e-187, 8.9953478731039129e-19, 2.1492251770664035e-127,                  0.11623743456915718, 6.182421882338173e-13, 3.7920992534448722e-112, 3.5888837913096105e-97,                  7.5565994692959197e-134, 0.0026669650138866794, 4.0844220972198632e-16, 4.583685848455044e-95] results= stats.combine_pvalues(p_value_list) print (results) 

Which gives the following output (p is zero!)

(7172.864625387464, 0.0) 

Now, my questions are:

  • What is the meaning of small p-values (something like 1.2e-187)?
  • How to interpret a p-value that is zero?
  • Since I am writing a paper based on this calculation, how can one report this in a peer-reviewed journal?
  1. A P-value of $10^{-187}$ is very strong evidence that the null hypothesis is not true, or that the statistical model used is inappropriate. Very strong indeed.

  2. A P-value of zero occurs when the report runs out of decimal places. A P-value of $10^{-187}$ is not really different from a P-value of zero for most purposes.

  3. Present the data and a measure of the effect size in addition to the P-values.

Similar Posts:

Rate this post

Leave a Comment