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Hypergeometric distribution excess kurtosis.

Imagine a scenario with a population of size N, of which a subpopulation of size K can be considered successes. We draw n observations from the total population. Defining the random variable X as the number of successes in the n draws, X is said to follow a hypergeometric distribution. The excess kurtosis for a hypergeometric random variable is

Excess kurtosis for a hypergeometric distribution.


npm install @stdlib/stats-base-dists-hypergeometric-kurtosis


var kurtosis = require( '@stdlib/stats-base-dists-hypergeometric-kurtosis' );

kurtosis( N, K, n )

Returns the excess kurtosis of a hypergeometric distribution with parameters N (population size), K (subpopulation size), and n (number of draws).

var v = kurtosis( 16, 11, 4 );
// returns ~-0.326

v = kurtosis( 4, 2, 2 );
// returns 0.0

If provided NaN as any argument, the function returns NaN.

var v = kurtosis( NaN, 10, 4 );
// returns NaN

v = kurtosis( 20, NaN, 4 );
// returns NaN

v = kurtosis( 20, 10, NaN );
// returns NaN

If provided a population size N, subpopulation size K, or draws n which is not a nonnegative integer, the function returns NaN.

var v = kurtosis( 10.5, 5, 2 );
// returns NaN

v = kurtosis( 10, 1.5, 2 );
// returns NaN

v = kurtosis( 10, 5, -2.0 );
// returns NaN

If the number of draws n or the subpopulation size K exceed population size N, the function returns NaN.

var v = kurtosis( 10, 5, 12 );
// returns NaN

v = kurtosis( 10, 12, 5 );
// returns NaN


var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var kurtosis = require( '@stdlib/stats-base-dists-hypergeometric-kurtosis' );

var v;
var i;
var N;
var K;
var n;

for ( i = 0; i < 10; i++ ) {
    N = round( randu() * 20 );
    K = round( randu() * N );
    n = round( randu() * K );
    v = kurtosis( N, K, n );
    console.log( 'N: %d, K: %d, n: %d, Kurt(X;N,K,n): %d', N, K, n, v.toFixed( 4 ) );


This package is part of 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, see the main project repository.






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