Z score to p values

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lou 2016-12-7

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评论： Germano Gallicchio 2024-2-28

I have a large matrix 1xN containing z values. I would like to know how to turn these z scores to p values using normcdf function?

How to obtain p values both for one-tailed and two-tailed p values using normcdf?

Many thanks in advance!

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Star Strider 2016-12-7

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If I remember correctly, the probability of a one-tailed test is twice the probability of a two-tailed test, so:

p_one = 2*normcdf(z_vector);
p_two = normcdf(z_vector);

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Star Strider 2017-3-30

It depends upon the hypothesis you are testing.

Noah 2021-7-23

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Note that the accepted answer is backwards, unless you mean something strange by your hypothesis. The probability of one-tailed test is HALF the probability of a two-tailed test. The area under a bell curve on one side is half the area on both sides.

p_oneTailed = normcdf(z_vector);
p_twoTailed = 2*normcdf(z_vector);

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Ziwei Liu 2023-8-18

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https://ww2.mathworks.cn/matlabcentral/answers/315844-z-score-to-p-values#answer_1289242

Two-tailed p value should actually be 2 * (1 - normcdf(z)).

normcdf(z) gives the area under curve on the left side of z. This is not p value. One-tailed p value should be the area on the right side, which is (1 - normcdf(z)).Two-tailed p value should be the double of that.

You can use the arrayfun function to compute p value for each entry in your z score matrix. i.e. p = arrayfun(@(x) 2*(1-normcdf(x)), ZScoreMatrix).

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Germano Gallicchio 2024-2-28

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For this to work with negative z scores, you also need to take the absolute value of z:

z = [-2.58 -1.96 -1.65 0 1.65 1.96 2.58]; % vector of z scores
p = 2 * (1 - normcdf(abs(z))); % vector of associated pvalues
disp([z' p'])
   -2.5800    0.0099
   -1.9600    0.0500
   -1.6500    0.0989
         0    1.0000
    1.6500    0.0989
    1.9600    0.0500
    2.5800    0.0099

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