{"library":"ttest","type":"library","category":null,"description":"ttest is a focused JavaScript library for performing Student's t-hypothesis tests. It supports both one-sample and two-sample t-tests, and accommodates scenarios with both equal and unequal variances by defaulting to Welch's t-test for two-sample cases. The library offers flexibility in data input, accepting raw arrays of values, `Summary` objects, or plain objects containing `mean`, `variance`, and `size`. Users can configure critical test parameters such as the null hypothesis mean (`mu`), the significance level (`alpha`), and the alternative hypothesis (e.g., 'less', 'greater', 'not equal'). The current stable version is 4.0.0. It provides a programmatic API to retrieve key statistical outcomes including the t-value (`testValue`), p-value (`pValue`), confidence interval (`confidence`), and degrees of freedom (`freedom`), along with a `valid()` method to check significance against alpha. Its main differentiator is its concise API specifically tailored for t-test computations.","language":"javascript","status":"active","version":"4.0.0","tags":["javascript","hypothesis","student t"],"last_verified":"Wed May 27","install":[{"cmd":"npm install ttest","imports":["const ttest = require('ttest')","import ttest from 'ttest'"]},{"cmd":"yarn add ttest","imports":[]},{"cmd":"pnpm add ttest","imports":[]}],"homepage":null,"github":"https://github.com/AndreasMadsen/ttest","docs":null,"changelog":null,"pypi":null,"npm":"https://www.npmjs.com/package/ttest","openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"18–22","success_rate":0,"avg_install_s":null,"avg_import_s":null,"wheel_type":null},"url":"https://checklist.day/v1/registry/ttest/compatibility"}}