In a data-driven world, your analyses will only ever be as good as the metric you use to evaluate them. In this post, I make the claim that the de facto metric used in data science is unfit for purpose and and can lead to the construction of unethical models. If this is the case, what should we use instead?

Not all integrals are created equally. In this post we look at a particular class of integrals which can be highly troublesome to evaluate. Thankfully, probability theory provides us with a framework that allows us to avoid the standard method of evaluation and by doing so makes our working far less error-prone.

Using an old number-pad mobile phone to send a text has to be one of the most strenuous and irritating tasks of the first world. In this post - although far too late to be of any use - I attempt to optimise the layout used on these phones to increase typing efficiency for a range of European languages.

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.