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.

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.

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