diff --git a/lib/node_modules/@stdlib/stats/base/dists/poisson/README.md b/lib/node_modules/@stdlib/stats/base/dists/poisson/README.md
index eee4ecf6770a..f8cae2dacec0 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/poisson/README.md
+++ b/lib/node_modules/@stdlib/stats/base/dists/poisson/README.md
@@ -111,10 +111,42 @@ y = dist.pmf( 2.3 );
 <!-- eslint no-undef: "error" -->
 
 ```javascript
-var objectKeys = require( '@stdlib/utils/keys' );
 var poisson = require( '@stdlib/stats/base/dists/poisson' );
 
-console.log( objectKeys( poisson ) );
+/*
+* Let's take a customer service center example: average rate of customer inquiries is 3 per hour.
+* This situation can be modeled using a Poisson distribution with λ = 3
+*/
+
+var lambda = 3;
+
+// Mean can be used to calculate the average number of inquiries per hour:
+console.log( poisson.mean( lambda ) );
+// => 3
+
+// Standard deviation can be used to calculate the measure of the spread of inquiries around the mean:
+console.log( poisson.stdev( lambda ) );
+// => ~1.7321
+
+// Variance can be used to calculate the variability of the number of inquiries:
+console.log( poisson.variance( lambda ) );
+// => 3
+
+// PMF can be used to calculate specific number of inquiries in an hour:
+console.log( poisson.pmf( 4, lambda ) );
+// => ~0.1680
+
+// CDF can be used to calculate probability upto certain number of inquiries in an hour:
+console.log( poisson.cdf( 2, lambda ) );
+// => ~0.4232
+
+// Quantile can be used to calculate the number of inquiries at which you can be 80% confident that the actual number will not exceed.
+console.log( poisson.quantile( 0.8, lambda ) );
+// => 4
+
+// MGF can be used for more advanced statistical analyses and generating moments of the distribution.
+console.log( poisson.mgf( 1.0, lambda ) );
+// => ~173.2690
 ```
 
 </section>
diff --git a/lib/node_modules/@stdlib/stats/base/dists/poisson/examples/index.js b/lib/node_modules/@stdlib/stats/base/dists/poisson/examples/index.js
index e382436ef33a..645008c52358 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/poisson/examples/index.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/poisson/examples/index.js
@@ -18,7 +18,39 @@
 
 'use strict';
 
-var objectKeys = require( '@stdlib/utils/keys' );
 var poisson = require( './../lib' );
 
-console.log( objectKeys( poisson ) );
+/*
+* Let's take a customer service center example: average rate of customer inquiries is 3 per hour.
+* This situation can be modeled using a Poisson distribution with λ = 3
+*/
+
+var lambda = 3;
+
+// Mean can be used to calculate the average number of inquiries per hour:
+console.log( poisson.mean( lambda ) );
+// => 3
+
+// Standard deviation can be used to calculate the measure of the spread of inquiries around the mean:
+console.log( poisson.stdev( lambda ) );
+// => ~1.7321
+
+// Variance can be used to calculate the variability of the number of inquiries:
+console.log( poisson.variance( lambda ) );
+// => 3
+
+// PMF can be used to calculate specific number of inquiries in an hour:
+console.log( poisson.pmf( 4, lambda ) );
+// => ~0.1680
+
+// CDF can be used to calculate probability upto certain number of inquiries in an hour:
+console.log( poisson.cdf( 2, lambda ) );
+// => ~0.4232
+
+// Quantile can be used to calculate the number of inquiries at which you can be 80% confident that the actual number will not exceed.
+console.log( poisson.quantile( 0.8, lambda ) );
+// => 4
+
+// MGF can be used for more advanced statistical analyses and generating moments of the distribution.
+console.log( poisson.mgf( 1.0, lambda ) );
+// => ~173.2690