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chi-square-a-b-testing
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Npm module for calculating chi-square test that gives us p-value for statistical significance with practical use in A/B testing.
`npm install --save-dev chi-square-a-b-testing`
Let's consider we have two ad variations - each variation has been displayed 50 times. The first ad was clicked 1, whereas the second ad was clicked 5 times. Is the difference statistically significant? Let's find out using chi-square test
* * * * * * * * * * *
* SAMPLE | CLICKED *
* 50 | 1 * - ad variation 1
* 50 | 5 * - ad variation 2
* * * * * * * * * * *
var test = require('chi-square-a-b-testing');
// Set up our sample values
const table = [
[50, 1],
[50, 5]
];
// Calculate the p-value
let pValue = test(table); // 0.9078770365273039
In this case the pValue = 0.9078770365273039
, which means we cannot consider the difference between those two variations statistically significant if we set our threshold to 0.5 (which is usually the case for a standard experiment).
The JavaScript implementation of chi-square test was done by http://stats.theinfo.org/ (Aaron Swartz and Ben Wikler)
FAQs
Npm module for calculating chi-square test that gives us p-value for statistical significance with practical use in A/B testing.
The npm package chi-square-a-b-testing receives a total of 14,446 weekly downloads. As such, chi-square-a-b-testing popularity was classified as popular.
We found that chi-square-a-b-testing demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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