Contents
Does it make sense to average 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.
How does p-value compare to significance level?
The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.
- A p-value less than 0.05 (typically ≤ 0.05) is statistically significant.
- A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis.
How to compare two p-value distributions in practice?
For instance, if we want to test whether a p-value distribution is uniformly distributed (i.e. p-value uniformity test) or not, we can simulate uniform random variables and compute the KS test statistic. By repeating this process 1000 times, we will have 1000 KS test statistics, which gives us the KS test statistic distribution below.
How do you find the p value of a statistic?
The second approach we can use to determine if our results are statistically significant is to find the p-value for the test statistic t of 1.34. In order to find this p-value, we can’t use the t distribution table because it only provides us with critical values, not p-values.
How to compare a sample with a distribution?
When we compare a sample with a theoretical distribution, we can use a Monte Carlo simulation to create a test statistics distribution. For instance, if we want to test whether a p-value distribution is uniformly distributed (i.e. p-value uniformity test) or not, we can simulate uniform random variables and compute the KS test statistic.
What does it mean when p value is not significant?
If neither p-value is significant, it suggests that the variable does not systematically tend to increase or decrease over time. If the nonlinear p-value is significant but not the linear p-value, it suggests you have variable rates of change in the short term but in the long run there is no systematic increase or decrease in the variable.