Reporting permutation test results

1. How do you report permutation test results?
2. What does a permutation test tell you?
3. What is the null hypothesis for permutation test?
4. How do you find the p-value from a permutation test in R?

How do you report permutation test results?

To denote permuted results, we will add a * to the labels: T^*=x_{Unattractive*}−x_Average*. We then compare the T_obs=x_Unattractive−x_Average= 1.84 to the distribution of results that are possible for the permuted results (T*) which corresponds to assuming the null hypothesis is true.

What does a permutation test tell you?

The purpose of a permutation test is to estimate the population distribution, the distribution where our observations came from. From there, we can determine how rare our observed values are relative to the population.

What is the null hypothesis for permutation test?

In effect, the idea of using the permutations to approach the null distribution relies on the assumption of data exchangeability under H0. This means that the null hypothesis always needs to be that the two samples are drawn from the same underlying distribution. Hence, if we have two independent samples X1,…,Xnxi.

How do you find the p-value from a permutation test in R?

P-value = No. of permutations having a test-stat value greater than observed test-stat value/ No. of permutations.