From 781389663cb9740782e04b031df74fe30bbb525f Mon Sep 17 00:00:00 2001
From: Athan <kgryte+claude@gmail.com>
Date: Tue, 18 Aug 2026 06:21:37 +0000
Subject: [PATCH] test: migrate `stats/base/dists/hypergeometric/variance` to
 ULP-based assertions

Replace computed relative-tolerance comparisons with `isAlmostSameValue`
ULP-based assertions in the fixture loops.

The JavaScript and C implementations evaluate the same variance formula
using different associations, so they require different bounds. The
JavaScript implementation computes `n*(K/N)*((N-K)/N)*((N-n)/(N-1))`,
whereas the C implementation computes `n*p*(1-p)*((N-n)/(N-1))`, the
latter matching the association used by the Julia reference
implementation which generated the fixtures.

Bounds are the measured minima over the full fixture set: 29 ULP for
test.js and 1 ULP for test.native.js. One lower fails in each case.

Ref: https://github.com/stdlib-js/stdlib/issues/11352
---
 .../base/dists/hypergeometric/variance/test/test.js | 13 ++-----------
 .../hypergeometric/variance/test/test.native.js     | 13 ++-----------
 2 files changed, 4 insertions(+), 22 deletions(-)

diff --git a/lib/node_modules/@stdlib/stats/base/dists/hypergeometric/variance/test/test.js b/lib/node_modules/@stdlib/stats/base/dists/hypergeometric/variance/test/test.js
index bfdf1fe386ba..8ff9c0b139ad 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/hypergeometric/variance/test/test.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/hypergeometric/variance/test/test.js
@@ -21,10 +21,9 @@
 // MODULES //
 
 var tape = require( 'tape' );
+var isAlmostSameValue = require( '@stdlib/assert/is-almost-same-value' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
-var abs = require( '@stdlib/math/base/special/abs' );
 var PINF = require( '@stdlib/constants/float64/pinf' );
-var EPS = require( '@stdlib/constants/float64/eps' );
 var variance = require( './../lib' );
 
 
@@ -119,8 +118,6 @@ tape( 'if provided an `n` which is not a nonnegative integer, the function retur
 
 tape( 'the function returns the variance of a hypergeometric distribution', function test( t ) {
 	var expected;
-	var delta;
-	var tol;
 	var N;
 	var K;
 	var n;
@@ -133,13 +130,7 @@ tape( 'the function returns the variance of a hypergeometric distribution', func
 	n = data.n;
 	for ( i = 0; i < n.length; i++ ) {
 		y = variance( N[i], K[i], n[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'N: '+N[i]+', K: '+K[i]+', n: '+n[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			delta = abs( y - expected[ i ] );
-			tol = 18.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. N: '+N[i]+'. K: '+K[i]+'. n: '+n[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
-		}
+		t.strictEqual( isAlmostSameValue( y, expected[ i ], 29 ), true, 'returns expected value' );
 	}
 	t.end();
 });
diff --git a/lib/node_modules/@stdlib/stats/base/dists/hypergeometric/variance/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/hypergeometric/variance/test/test.native.js
index b4a3375fd6ee..1a6b84f0f6c2 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/hypergeometric/variance/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/hypergeometric/variance/test/test.native.js
@@ -23,9 +23,8 @@
 var resolve = require( 'path' ).resolve;
 var tape = require( 'tape' );
 var tryRequire = require( '@stdlib/utils/try-require' );
+var isAlmostSameValue = require( '@stdlib/assert/is-almost-same-value' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
-var abs = require( '@stdlib/math/base/special/abs' );
-var EPS = require( '@stdlib/constants/float64/eps' );
 
 
 // VARIABLES //
@@ -87,8 +86,6 @@ tape( 'if provided an `n` which is not a nonnegative integer, the function retur
 
 tape( 'the function returns the variance of a hypergeometric distribution', opts, function test( t ) {
 	var expected;
-	var delta;
-	var tol;
 	var N;
 	var K;
 	var n;
@@ -101,13 +98,7 @@ tape( 'the function returns the variance of a hypergeometric distribution', opts
 	n = data.n;
 	for ( i = 0; i < n.length; i++ ) {
 		y = variance( N[i], K[i], n[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'N: '+N[i]+', K: '+K[i]+', n: '+n[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			delta = abs( y - expected[ i ] );
-			tol = 18.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. N: '+N[i]+'. K: '+K[i]+'. n: '+n[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
-		}
+		t.strictEqual( isAlmostSameValue( y, expected[ i ], 1 ), true, 'returns expected value' );
 	}
 	t.end();
 });