# The Poisson Distribution Meets Modular Arithmetic

Inspired by a simple probability puzzle, I set out to determine the probability that a Poisson random variable is divisible by a given integer, before extending this result to calculate the distribution of the Poisson random variable modulo a divisor.

# Gibrat's Law: The Central Limit Theorem's Forgotten Twin

Hiding in the shadow of the central limit theorm is a lesser-know, but still fascinating aspect of statistics. Read this post to what this is and how we can use it.

# A Statistican's Guide to Love

Struggling to find 'the one'? Perhaps statistics can help. In this post we introduce a pair of simple procedures which—when followed—can optimise your chances of finding love.

# #AdventGate—The shocking secret Big Advent doesn't want you to know

Don't waste your tin foil wrapping up your turkey when fashioning it into a hat is far more in need this Christmas. In this post I will reveal how advent calendar makers the world over, have been lying to you about. So launch your VPN and delete your cookies, we're about to take down Big Advent.

# Generating Normal Random Variables - Part 1: Inverse Transform Sampling

The normal distribution is one of the most important developments in the history of statistics. As well as its useful statistical properties, it is so well-loved for its omnipresence in the natural world, appearing in all sorts of contexts from epidemiology to quantum mechanics. This blog post, the first in a series of posts discussing how we can generate random normal variables, explores the theory behind and the implementation of inverse transform sampling.

# A Statistican's Guide to Playing Darts

Although the game of darts requires a tremendous amount of skill to be a good player, there is still a very large probabilistic element. In this post, we take undertake a stochastic analysis of the game in order to reach an optimum strategy for play depending on the typical accuarcy of your shots.

# Binomial Recursion

In this post, we take a simple coin-flipping puzzle and through scope-expansion and generalisation, turn it into a monster probability problem that we can be proud to have tackled. In it, we look at some clever techiques for calculating probabilities which are vital in any experienced statistian's toolbox.
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