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Can you take the average of p-values?
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.
Does P 0.05 mean significant?
P > 0.05 is the probability that the null hypothesis is true. A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected. A P value greater than 0.05 means that no effect was observed.
What does the p-value cutoff mean?
In the majority of analyses, an alpha of 0.05 is used as the cutoff for significance. If the p-value is less than 0.05, we reject the null hypothesis that there’s no difference between the means and conclude that a significant difference does exist. Below 0.05, significant. Over 0.05, not significant.
How does p-value affect mean?
When there is a meaningful null hypothesis, the strength of evidence against it should be indexed by the P value. The smaller the P value, the stronger is the evidence.
What does P value stand for?
In statistics, the p-value is the probability of obtaining results at least as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct. A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis.
What do you need to know about the p value?
This leads us to the first common misinterpretation of the p-value: A p-value is NOT the probability that the null hypothesis is true. For example, if we test the effect of our drug against a placebo and obtain p = 0.01, does that mean there is only a 1% chance that our drug is ineffective (and therefore a 99% chance that it is effective)?
When to put a zero before a p value?
One last tip: Many authorities in scientific, technical, and medical fields recommend that a zero should not be inserted before a decimal fraction when the number cannot be greater than 1 (e.g., correlations, proportions, and levels of statistical significance); that is, “p < 0.05” should be written as “p < .05.”.
When to report p values less than.001?
For all P values less than .001 report them as P <.001, instead of the actual exact P value. Expressing P to more than 3 significant digits does not add useful information since precise P values with extreme results are sensitive to biases or departures from the statistical model.
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.