diff --git a/lib/node_modules/@stdlib/stats/base/dists/triangular/README.md b/lib/node_modules/@stdlib/stats/base/dists/triangular/README.md
index d98eabf8970d..ae6ba01ba8b3 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/triangular/README.md
+++ b/lib/node_modules/@stdlib/stats/base/dists/triangular/README.md
@@ -107,15 +107,62 @@ y = dist.quantile( 1.9 );
 
 ## Examples
 
-<!-- TODO: better examples -->
-
 <!-- eslint no-undef: "error" -->
 
 ```javascript
-var objectKeys = require( '@stdlib/utils/keys' );
+var discreteUniform = require( '@stdlib/random/base/discrete-uniform' );
 var triangular = require( '@stdlib/stats/base/dists/triangular' );
 
-console.log( objectKeys( triangular ) );
+// Scenario: Modeling completion time for a software development task
+
+// Define the distribution parameters (in hours):
+var a = 1.5; // Minimum time (best-case scenario)
+var b = 4.5; // Maximum time (worst-case scenario)
+var c = discreteUniform( 2, 4 ); // Most likely time (mode)
+console.log( 'a: %d, b: %d, c: %d', a, b, c );
+
+// Expected (mean) completion time:
+var mean = triangular.mean( a, b, c );
+console.log( '\nExpected completion time: %d hours', mean );
+
+// Median completion time:
+var median = triangular.median( a, b, c );
+console.log( 'Median completion time: %d hours', median );
+
+// Variance in completion time:
+var variance = triangular.variance( a, b, c );
+console.log( 'Variance in completion time: %d hours^2', variance );
+
+// Probability of completing the task within 3 hours:
+var x = 3.0;
+var prob = triangular.cdf( x, a, b, c );
+console.log( '\nProbability of completing within %d hours: %d', x, prob );
+
+// 90th percentile of completion time:
+var p = 0.9;
+var percentile = triangular.quantile( p, a, b, c );
+console.log( '90% of tasks will be completed within %d hours', percentile );
+
+// Relative likelihood of completing the task in exactly 2.5 hours:
+x = 2.5;
+var likelihood = triangular.pdf( x, a, b, c );
+console.log( '\nRelative likelihood of completing in exactly %d hours: %d', x, likelihood );
+
+// Skewness to understand the distribution's shape:
+var skewness = triangular.skewness( a, b, c );
+console.log( '\nSkewness of completion times: %d', skewness );
+if ( skewness > 0 ) {
+    console.log( 'The distribution is right-skewed, suggesting occasional longer completion times.' );
+} else if ( skewness < 0 ) {
+    console.log( 'The distribution is left-skewed, suggesting occasional shorter completion times.' );
+} else {
+    console.log( 'The distribution is symmetric.' );
+}
+
+// Entropy as a measure of uncertainty in the estimate:
+var entropy = triangular.entropy( a, b, c );
+console.log( '\nEntropy of the distribution: %d nats', entropy );
+console.log( 'Higher entropy indicates more uncertainty in completion times.' );
 ```
 
 </section>
diff --git a/lib/node_modules/@stdlib/stats/base/dists/triangular/examples/index.js b/lib/node_modules/@stdlib/stats/base/dists/triangular/examples/index.js
index aeb22ffc0745..f3690a519842 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/triangular/examples/index.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/triangular/examples/index.js
@@ -18,7 +18,56 @@
 
 'use strict';
 
-var objectKeys = require( '@stdlib/utils/keys' );
+var discreteUniform = require( '@stdlib/random/base/discrete-uniform' );
 var triangular = require( './../lib' );
 
-console.log( objectKeys( triangular ) );
+// Scenario: Modeling completion time for a software development task
+
+// Define the distribution parameters (in hours):
+var a = 1.5; // Minimum time (best-case scenario)
+var b = 4.5; // Maximum time (worst-case scenario)
+var c = discreteUniform( 2, 4 ); // Most likely time (mode)
+console.log( 'a: %d, b: %d, c: %d', a, b, c );
+
+// Expected (mean) completion time:
+var mean = triangular.mean( a, b, c );
+console.log( '\nExpected completion time: %d hours', mean );
+
+// Median completion time:
+var median = triangular.median( a, b, c );
+console.log( 'Median completion time: %d hours', median );
+
+// Variance in completion time:
+var variance = triangular.variance( a, b, c );
+console.log( 'Variance in completion time: %d hours^2', variance );
+
+// Probability of completing the task within 3 hours:
+var x = 3.0;
+var prob = triangular.cdf( x, a, b, c );
+console.log( '\nProbability of completing within %d hours: %d', x, prob );
+
+// 90th percentile of completion time:
+var p = 0.9;
+var percentile = triangular.quantile( p, a, b, c );
+console.log( '90% of tasks will be completed within %d hours', percentile );
+
+// Relative likelihood of completing the task in exactly 2.5 hours:
+x = 2.5;
+var likelihood = triangular.pdf( x, a, b, c );
+console.log( '\nRelative likelihood of completing in exactly %d hours: %d', x, likelihood );
+
+// Skewness to understand the distribution's shape:
+var skewness = triangular.skewness( a, b, c );
+console.log( '\nSkewness of completion times: %d', skewness );
+if ( skewness > 0 ) {
+	console.log( 'The distribution is right-skewed, suggesting occasional longer completion times.' );
+} else if ( skewness < 0 ) {
+	console.log( 'The distribution is left-skewed, suggesting occasional shorter completion times.' );
+} else {
+	console.log( 'The distribution is symmetric.' );
+}
+
+// Entropy as a measure of uncertainty in the estimate:
+var entropy = triangular.entropy( a, b, c );
+console.log( '\nEntropy of the distribution: %d nats', entropy );
+console.log( 'Higher entropy indicates more uncertainty in completion times.' );