calculate a P value using a permutation test
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How can I calculate a P value using a permutation test for these 2 groups?
controlA=[0.22, -0.87, -2.39, -1.79, 0.37, -1.54, 1.28, -0.31, -0.74, 1.72, 0.38, -0.17, -0.62, -1.10, 0.30, 0.15, 2.30, 0.19, -0.50, -0.09];
treatmentA=[-5.13, -2.19, -2.43, -3.83, 0.50, -3.25, 4.32, 1.63, 5.18, -0.43, 7.11, 4.87, -3.10, -5.81, 3.76, 6.31, 2.58, 0.07, 5.76, 3.50];
I know that both samples come from the same distribution.
thanks
1 Comment
Shuting Li
on 1 Feb 2021
permutationTest(controlA, treatmentA, 10000, 'plotresult', 1);
and the permutation code is in https://github.com/lrkrol/permutationTest.git
Answers (2)
Star Strider
on 11 Jan 2020
For your data:
p =
0.350702223666955
h =
0
stats =
zval: -0.933228071880792
ranksum: 375
So the ‘p’ value indicates that they are not significantly different, and ‘h’ indicates that the null hypothesis is not rejected.
3 Comments
Star Strider
on 11 Jan 2020
I have never heard of the ’permutation test’. When I did an Interweb search on it, the Wilcoxon rank-sum test came up in several examples. That is the reason I use it here.
BINNAN YU
on 7 May 2020
permutation test is equivalent to wilcoxon rank-sum test, but two are different test
Jeff Miller
on 12 Jan 2020
This contribution on file exchange looks like it will do what you want.
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