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Can p-values be averaged?
For your original question, it does not make sense to average p-values because an average p-value has no useful interpretation for your needs (or for anything that I am aware of). What you are trying to do, test whether the distances between locations follow some distribution, is exactly what the KS test does.
Can you add p-values together?
Under Fisher’s method, two small p-values P1 and P2 combine to form a smaller p-value. For example, if both p-values are around 0.10, or if one is around 0.04 and one is around 0.25, the meta-analysis p-value is around 0.05.
Should p-values be rounded?
P is always italicized and capitalized. The actual P value* should be expressed (P=. 01 then the P value should always be expressed to 2 digits whether or not it is significant. When rounding, 3 digits is acceptable if rounding would change the significance of a value (eg, you may write P=.
Under what circumstances might we want to combine P-values?
Combining p-values is usually required in one of two situations: (1) when either the values of the actual statistics that need to be combined or the forms of their distributions are unknown, or (2) this information is available, but the distributions are such that there is no known or reasonably convenient method …
Is p .001 statistically significant?
Most authors refer to statistically significant as P < 0.05 and statistically highly significant as P < 0.001 (less than one in a thousand chance of being wrong).
Are there any papers on combining p-values?
E.g. a recent overview Cousins (2008) Annotated Bibliography of Some Papers on Combining Significances or p-values does not mention Edgington’s method at all and it seems that this term has never been mentioned on CrossValidated either.
Why are p-values not really probabilities?
My intuition is different. Multiple similar results would seem to make the significance higher (and therefore the p-values which are probabilities should be lower). P-values are not really probabilities. They are statements about the sample distribution of observed values under a particular hypothesis.
How are p-values combined in a joint test?
All these p-values can be combined into a joint test whether there is a global effect, i.e., if a global null hypothesis can be rejected. There are a number of ways to combine these independent, partial tests. The Fisher method is one of these, and is perhaps the most famous and most widely used.
Is it possible to get multiple extreme p-values?
At any rate, under the null hypothesis of no difference, the chances of getting multiple extreme p-values would seem to be much more unlikely. Every time I see the statement that the p-value is uniformly distributed from 0-1 under the null hypothesis I feel compelled to test it with simulation, and so far the statement seems to hold.