# 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?
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