<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Probability Cards blog</title><link>https://probabilitycards.com/blog</link><description>Free probability lessons, worksheets, a counting calculator and simulators that use an ordinary 52-card deck to teach combinatorics, probability, Monte Carlo simulation, randomness and sampling error. Educational only, no gambling.</description><language>en</language><item><title>How many five-card hands are there? The 2,598,960 explained</title><link>https://probabilitycards.com/blog/how-many-five-card-hands-are-there</link><guid>https://probabilitycards.com/blog/how-many-five-card-hands-are-there</guid><pubDate>Tue, 03 Nov 2026 00:00:00 GMT</pubDate><description>Why a 52-card deck makes exactly 2,598,960 different five-card hands, shown step by step with permutations, combinations and a simple divide-by-120 trick.</description></item><item><title>Five-card poker hand probabilities: the full table and how each is counted</title><link>https://probabilitycards.com/blog/poker-hand-probabilities-table</link><guid>https://probabilitycards.com/blog/poker-hand-probabilities-table</guid><pubDate>Tue, 03 Nov 2026 00:00:00 GMT</pubDate><description>Exact counts and probabilities for all nine five-card hand types, from straight flush to no pattern, with the counting argument behind every number.</description></item><item><title>Why is one pair so common? Counting versus intuition</title><link>https://probabilitycards.com/blog/why-one-pair-is-so-common</link><guid>https://probabilitycards.com/blog/why-one-pair-is-so-common</guid><pubDate>Tue, 03 Nov 2026 00:00:00 GMT</pubDate><description>One pair appears in over 42% of five-card hands while a flush is under 0.2%. The reason is counting: how many hands fit each pattern, not how special they look.</description></item><item><title>What is a Monte Carlo simulation? A card-deck explanation</title><link>https://probabilitycards.com/blog/what-is-monte-carlo-simulation</link><guid>https://probabilitycards.com/blog/what-is-monte-carlo-simulation</guid><pubDate>Tue, 03 Nov 2026 00:00:00 GMT</pubDate><description>Monte Carlo simulation estimates probabilities by repeating a random experiment many times. See how it works with a deck of cards and when it can be trusted.</description></item><item><title>The law of large numbers, shown with playing cards</title><link>https://probabilitycards.com/blog/law-of-large-numbers-with-cards</link><guid>https://probabilitycards.com/blog/law-of-large-numbers-with-cards</guid><pubDate>Tue, 03 Nov 2026 00:00:00 GMT</pubDate><description>Why experiments settle toward the true probability as samples grow, why the error shrinks like one over the square root of n, and how to see it with 100 to 100,000 deals.</description></item><item><title>Permutations vs combinations: a simple way to tell them apart</title><link>https://probabilitycards.com/blog/permutations-vs-combinations</link><guid>https://probabilitycards.com/blog/permutations-vs-combinations</guid><pubDate>Tue, 03 Nov 2026 00:00:00 GMT</pubDate><description>Does order matter? A clear guide to choosing between permutations and combinations, with card examples, formulas and common mistakes.</description></item><item><title>Why real randomness looks streaky</title><link>https://probabilitycards.com/blog/why-random-looks-streaky</link><guid>https://probabilitycards.com/blog/why-random-looks-streaky</guid><pubDate>Tue, 03 Nov 2026 00:00:00 GMT</pubDate><description>True random sequences contain surprising runs and clusters. Learn why streaks are normal, why 'due' outcomes do not exist, and how shuffles and seeds work.</description></item></channel></rss>