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mann-whitney-utest

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The Mann-Whitney U test is a nonparametric statistical test

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# Mann-Whitney U Test [![Build Status](https://travis-ci.org/lukem512/mann-whitney-utest.svg?branch=master)](https://travis-ci.org/lukem512/mann-whitney-utest) ![Build Status](https://david-dm.org/lukem512/mann-whitney-utest.svg) [![npm](https://img.shields.io/npm/l/mann-whitney-utest.svg)](https://www.npmjs.com/package/mann-whitney-utest) [![npm](https://img.shields.io/npm/v/mann-whitney-utest.svg)](https://www.npmjs.com/package/mann-whitney-utest) [![npm](https://img.shields.io/npm/dm/mann-whitney-utest.svg)](https://www.npmjs.com/package/mann-whitney-utest) This is an NPM module that allows you to perform the Mann-Whitney U test on numeric samples. The Mann-Whitney U test is a nonparametric statistical test that does not assume a normal distribution. To use it, simply install via NPM and include it in your project file. ``` var mwu = require('mann-whitney-utest'); ``` Then, to test an array of samples, use the `test` method. ``` var samples = [ [30, 14, 6], [12, 15, 16] ]; console.log(mwu.test(samples)); // [ 4, 5 ] ``` To test whether the result is significant, use the `significant` method. This tests the U-value against an approximate critical value. ``` var u = mwu.test(samples); if (mwu.significant(u, samples)) { console.log('The data is significant!'); } else { console.log('The data is not significant.'); } ``` You can check your answers using the `check` method. This exploits a property of the Mann-Whitney test that ensures the sum of the U values does not exceed the product of the number of observations. ``` var u = mwu.test(samples); if (mwu.check(u, samples)) { console.log('The values are correct'); } ```