About

Author

Godwil

Hi, I’m Godwill

I’m a Health Data Science MSc student at the University of Galway, with a background in Operations Research and Statistics. I’ve worked as a Monitoring & Evaluation Specialist in public health and currently spend my days fitting Bayesian models and wrangling survey data.

Why this blog?

I’ve noticed something in the rush toward AI and machine learning: people want to run the algorithms but skip understanding why they work. The matrix algebra, the probability theory, the regression foundations, these aren’t just academic hurdles. They’re the difference between someone who uses tools and someone who understands them.

Stats Beneath is my attempt to bridge that gap. Each week, I take one statistical concept and explain it the way I wish it had been explained to me, with real-world analogies, clear visuals, and zero unnecessary jargon.

My approach

Every article follows a simple structure:

The real-world hook - why should you care?
The intuition - what’s actually going on, in plain language
The visual - a plot or diagram that makes it click
The code (optional) - simple R examples for those who want hands-on practice
The takeaway - one sentence you’ll remember

If you find these useful, share them with someone who thinks statistics is scary. It doesn’t have to be.

LinkedIn GitHub Email

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## Hi, I'm Godwill

I'm a Health Data Science MSc student at the University of Galway, with a background in Operations Research and Statistics. I've worked as a Monitoring & Evaluation Specialist in public health and currently spend my days fitting Bayesian models and wrangling survey data.

### Why this blog?

I've noticed something in the rush toward AI and machine learning: people want to run the algorithms but skip understanding *why* they work. The matrix algebra, the probability theory, the regression foundations, these aren't just academic hurdles. They're the difference between someone who uses tools and someone who understands them.

**Stats Beneath** is my attempt to bridge that gap. Each week, I take one statistical concept and explain it the way I wish it had been explained to me, with real-world analogies, clear visuals, and zero unnecessary jargon.

### My approach

Every article follows a simple structure:

1. **The real-world hook** - why should you care?
2. **The intuition** - what's actually going on, in plain language
3. **The visual** - a plot or diagram that makes it click
4. **The code** (optional) - simple R examples for those who want hands-on practice
5. **The takeaway** - one sentence you'll remember

If you find these useful, share them with someone who thinks statistics is scary. It doesn't have to be.