# ttest

4.0.0 • Public • Published

# ttest

Perform the Student t hypothesis test

## Installation

``````npm install ttest
``````

## Example

```var ttest = require('ttest');

// One sample t-test
ttest([0,1,1,1], {mu: 1}).valid() // true

// Two sample t-test
ttest([0,1,1,1], [1,2,2,2], {mu: -1}).valid() // true```

## Documentation

`var ttest = require('ttest');`

The `ttest` module supports both one and two sample t-testing, and both equal and none equal variance.

If one array of data is given its a one sample t-test, and if two data arrays are given its a two sample t-test.

`ttest()` supports data in the following format:

• an array of values, e.g. `ttest([1, 2, 3])`
• a `Summary` object, e.g. `ttest(new Summary([1, 2, 3]))`
• an object with the following properties: `mean`, `variance`, `size`, e.g. `ttest({mean: 123, variance: 1, size: 42})`

In all cases you can also pass an extra optional object, there takes the following properties:

```const options = {
// Default: 0
// One sample case: this is the µ that the mean will be compared with.
// Two sample case: this is the ∂ value that the mean diffrence will be compared with.
mu: Number,

// Default: false
// If false don't assume variance is equal and use the Welch approximation.
// This only applies if two samples are used.
varEqual: Boolean,

// Default: 0.05
// The significance level of the test
alpha: Number,

// Default "not equal"
// What should the alternative hypothesis be:
// - One sample case: could the mean be less, greater or not equal to mu property.
// - Two sample case: could the mean diffrence be less, greater or not equal to mu property.
alternative: "less" || "greater" || "not equal"
};```

The t-test object is finally created by calling the `ttest` constructor.

```const stat = ttest(sample, options);
const stat = ttest(sampleA, sampleB, options);```

When the `ttest` object is created you can get the following information.

##### stat.testValue()

Returns the `t` value also called the `statistic` value.

##### stat.pValue()

Returns the `p-value`.

##### stat.confidence()

Returns an array containing the confidence interval, where the confidence level is calculated as `1 - options.alpha`. Where the lower limit has index `0` and the upper limit has index `1`. If the alternative hypothesis is `less` or `greater` one of the sides will be `+/- Infinity`.

##### stat.valid()

Simply returns true if the `p-value` is greater or equal to the `alpha` value.

##### stat.freedom()

Returns the degrees of freedom used in the t-test.

## Package Sidebar

### Install

`npm i ttest`

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4.0.0

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