https://github.com/stdlib-js/stats-base-ndarray-dztest
Compute a one-sample Z-test for a one-dimensional double-precision floating-point ndarray.
https://github.com/stdlib-js/stats-base-ndarray-dztest
hypothesis javascript math mathematics ndarray node node-js nodejs normality statistics stats stdlib z-test ztest
Last synced: 18 days ago
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Compute a one-sample Z-test for a one-dimensional double-precision floating-point ndarray.
- Host: GitHub
- URL: https://github.com/stdlib-js/stats-base-ndarray-dztest
- Owner: stdlib-js
- License: apache-2.0
- Created: 2025-06-22T12:38:08.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2026-04-12T12:32:51.000Z (about 2 months ago)
- Last Synced: 2026-05-03T21:07:11.416Z (about 1 month ago)
- Topics: hypothesis, javascript, math, mathematics, ndarray, node, node-js, nodejs, normality, statistics, stats, stdlib, z-test, ztest
- Language: JavaScript
- Homepage: https://github.com/stdlib-js/stdlib
- Size: 1.9 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Security: SECURITY.md
- Notice: NOTICE
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README
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# dztest
[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]
> Compute a one-sample Z-test for a one-dimensional double-precision floating-point ndarray.
A Z-test commonly refers to a one-sample location test which compares the mean of a set of measurements `X` to a given constant when the standard deviation is known. A Z-test supports testing three different null hypotheses `H0`:
- `H0: μ ≥ μ0` versus the alternative hypothesis `H1: μ < μ0`.
- `H0: μ ≤ μ0` versus the alternative hypothesis `H1: μ > μ0`.
- `H0: μ = μ0` versus the alternative hypothesis `H1: μ ≠ μ0`.
## Installation
```bash
npm install @stdlib/stats-base-ndarray-dztest
```
Alternatively,
- To load the package in a website via a `script` tag without installation and bundlers, use the [ES Module][es-module] available on the [`esm`][esm-url] branch (see [README][esm-readme]).
- If you are using Deno, visit the [`deno`][deno-url] branch (see [README][deno-readme] for usage intructions).
- For use in Observable, or in browser/node environments, use the [Universal Module Definition (UMD)][umd] build available on the [`umd`][umd-url] branch (see [README][umd-readme]).
The [branches.md][branches-url] file summarizes the available branches and displays a diagram illustrating their relationships.
To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.
## Usage
```javascript
var dztest = require( '@stdlib/stats-base-ndarray-dztest' );
```
#### dztest( arrays )
Computes a one-sample Z-test for a one-dimensional double-precision floating-point ndarray.
```javascript
var Float64Results = require( '@stdlib/stats-base-ztest-one-sample-results-float64' );
var resolveEnum = require( '@stdlib/stats-base-ztest-alternative-resolve-enum' );
var structFactory = require( '@stdlib/array-struct-factory' );
var Float64Vector = require( '@stdlib/ndarray-vector-float64' );
var scalar2ndarray = require( '@stdlib/ndarray-from-scalar' );
var ndarray = require( '@stdlib/ndarray-ctor' );
var opts = {
'dtype': 'float64'
};
var x = new Float64Vector( [ 1.0, 3.0, 4.0, 2.0 ] );
var alt = scalar2ndarray( resolveEnum( 'two-sided' ), {
'dtype': 'int8'
});
var alpha = scalar2ndarray( 0.05, opts );
var mu = scalar2ndarray( 0.0, opts );
var sigma = scalar2ndarray( 1.0, opts );
var ResultsArray = structFactory( Float64Results );
var out = new ndarray( Float64Results, new ResultsArray( 1 ), [], [ 0 ], 0, 'row-major' );
var v = dztest( [ x, out, alt, alpha, mu, sigma ] );
var bool = ( v === out );
// returns true
```
The function has the following parameters:
- **arrays**: array-like object containing the following ndarrays:
- a one-dimensional input ndarray.
- a zero-dimensional output ndarray containing a [results object][@stdlib/stats/base/ztest/one-sample/results/float64].
- a zero-dimensional ndarray specifying the alternative hypothesis.
- a zero-dimensional ndarray specifying the significance level.
- a zero-dimensional ndarray specifying the mean under the null hypothesis.
- a zero-dimensional ndarray specifying the known standard deviation.
## Notes
- As a general rule of thumb, a Z-test is most reliable for sample sizes greater than `50`. For smaller sample sizes or when the standard deviation is unknown, prefer a t-test.
## Examples
```javascript
var Float64Results = require( '@stdlib/stats-base-ztest-one-sample-results-float64' );
var resolveEnum = require( '@stdlib/stats-base-ztest-alternative-resolve-enum' );
var structFactory = require( '@stdlib/array-struct-factory' );
var normal = require( '@stdlib/random-normal' );
var ndarray = require( '@stdlib/ndarray-ctor' );
var scalar2ndarray = require( '@stdlib/ndarray-from-scalar' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var dztest = require( '@stdlib/stats-base-ndarray-dztest' );
var opts = {
'dtype': 'float64'
};
// Create a one-dimensional ndarray containing pseudorandom numbers drawn from a normal distribution:
var x = normal( [ 100 ], 0.0, 1.0, opts );
console.log( ndarray2array( x ) );
// Specify the alternative hypothesis:
var alt = scalar2ndarray( resolveEnum( 'two-sided' ), {
'dtype': 'int8'
});
// Specify the significance level:
var alpha = scalar2ndarray( 0.05, opts );
// Specify the mean under the null hypothesis:
var mu = scalar2ndarray( 0.0, opts );
// Specify the known standard deviation:
var sigma = scalar2ndarray( 1.0, opts );
// Create a zero-dimensional results ndarray:
var ResultsArray = structFactory( Float64Results );
var out = new ndarray( Float64Results, new ResultsArray( 1 ), [], [ 0 ], 0, 'row-major' );
// Perform a Z-test:
var v = dztest( [ x, out, alt, alpha, mu, sigma ] );
console.log( v.get().toString() );
```
* * *
## Notice
This package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.
For more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].
#### Community
[![Chat][chat-image]][chat-url]
---
## License
See [LICENSE][stdlib-license].
## Copyright
Copyright © 2016-2026. The Stdlib [Authors][stdlib-authors].
[npm-image]: http://img.shields.io/npm/v/@stdlib/stats-base-ndarray-dztest.svg
[npm-url]: https://npmjs.org/package/@stdlib/stats-base-ndarray-dztest
[test-image]: https://github.com/stdlib-js/stats-base-ndarray-dztest/actions/workflows/test.yml/badge.svg?branch=main
[test-url]: https://github.com/stdlib-js/stats-base-ndarray-dztest/actions/workflows/test.yml?query=branch:main
[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/stats-base-ndarray-dztest/main.svg
[coverage-url]: https://codecov.io/github/stdlib-js/stats-base-ndarray-dztest?branch=main
[chat-image]: https://img.shields.io/badge/zulip-join_chat-brightgreen.svg
[chat-url]: https://stdlib.zulipchat.com
[stdlib]: https://github.com/stdlib-js/stdlib
[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors
[umd]: https://github.com/umdjs/umd
[es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules
[deno-url]: https://github.com/stdlib-js/stats-base-ndarray-dztest/tree/deno
[deno-readme]: https://github.com/stdlib-js/stats-base-ndarray-dztest/blob/deno/README.md
[umd-url]: https://github.com/stdlib-js/stats-base-ndarray-dztest/tree/umd
[umd-readme]: https://github.com/stdlib-js/stats-base-ndarray-dztest/blob/umd/README.md
[esm-url]: https://github.com/stdlib-js/stats-base-ndarray-dztest/tree/esm
[esm-readme]: https://github.com/stdlib-js/stats-base-ndarray-dztest/blob/esm/README.md
[branches-url]: https://github.com/stdlib-js/stats-base-ndarray-dztest/blob/main/branches.md
[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-base-ndarray-dztest/main/LICENSE
[@stdlib/stats/base/ztest/one-sample/results/float64]: https://github.com/stdlib-js/stats-base-ztest-one-sample-results-float64