Motivation
Statistics is one of the most powerful tools for understanding the world — and one of the most poorly taught. Too often it’s presented as a collection of procedures to follow rather than a coherent way of thinking about uncertainty. The mechanics get taught; the intuition doesn’t.
Much of my writing exists to change that, at least in a small way. Every post here is written with a single goal: to help you understand not just how a method works, but why it works and when you should trust it. The mathematics is taken seriously, but it’s always in service of understanding — never an end in itself.
If you leave a post feeling like something that was previously opaque has genuinely clicked, that’s the goal achieved.
Background
My path to statistics was not a straight line. I trained originally as a cognitive psychologist, completing a PhD focused on psychophysics, visual memory, and perception. Much of that work involved building and fitting mathematical models to understand how humans process sensory information — which meant getting very comfortable with probability, inference, and the question of what data can and cannot tell you.
From academia I moved into applied data science, working for New Zealand Police where the stakes of analytical decisions are very real. That experience — taking statistical thinking out of the controlled environment of a research lab and into messy, consequential, real-world problems — sharpened my understanding of where methods work, where they break down, and where good judgement has to fill the gap.
I recently returned to academia and am building models of food systems.
On Bayesian Inference
If there is one thread running through most of what I write, it is probability as a language for uncertainty. Bayesian inference is not just a set of methods to me — it is a coherent framework for reasoning under uncertainty that I find both intellectually compelling and practically powerful.
A recurring theme here will be building Bayesian intuition from the ground up: what it means to have a prior, what it means to update on evidence, and why that way of thinking changes how you approach data problems.
Get in Touch
If you have questions, suggestions, or just want to talk statistics, I’d love to hear from you.