The statistics behind the code.

Every model has a formula. Every formula has a reason. We break down the statistical foundations of analytics, machine learning, and AI. So you understand what's really happening under the hood.

Start reading

Why Statbitall

What you'll find here

Every post follows the same rigorous structure, so you never have to wonder what got skipped.

The math that matters

We don't skip the formulas. Every concept is explained with the core math, step by step, with every variable defined.

Code you can actually run

Every post includes minimal, clean Python that you can copy, paste, and execute. The code follows the math directly.

When to use it, and when not to

We cover assumptions, limitations, and real business scenarios so you know when a tool is right and when it isn't.

Every post.
Same structure.
No shortcuts.

Statbitall breaks down every concept the same way, so you always know what to expect, and nothing gets skipped.

  1. 01 The underlying idea. Plain language, no prerequisites
  2. 02 Historical root. Who built it, when, and why
  3. 03 Key assumptions. What must be true for it to work
  4. 04 The math. Core formulas, fully derived
  5. 05 The code. Clean Python you can run
  6. 06 Business application. When to use it, when not to

Learning Paths

Where should you start?

Pick the path that matches where you are right now.
01

Building the foundation

New to statistics or need a refresher? Start with probability, distributions, and the Central Limit Theorem. These concepts power everything else on this site.

Start with the basics
02

Testing and inference

Comfortable with the fundamentals? Learn how to design experiments, run hypothesis tests, and avoid the most common pitfalls in A/B testing and p-value interpretation.

Explore statistical tests
03

ML, explained statistically

Ready to see how machine learning models actually work? We break down regression, classification, clustering, and dimensionality reduction through a statistical lens.

Dive into ML models

Latest posts

Statistical Tests 12 min read

Hypothesis testing from scratch: the logic before the formula

Most courses start with p-values. That's the wrong place to start. Here's the logical framework that makes hypothesis testing coherent, before a single formula appears.
Statistical Tests 11 min read

A/B testing under the hood: what the platform isn't telling you

Why peeking at results inflates your false positive rate, how multiple metrics break your significance threshold, and the pre-experiment checklist that makes experiments trustworthy.
Statistical Tests 9 min read

Chi-square tests: how to make decisions from categories

When your data is counts, not measurements. The goodness-of-fit test, the test of independence, and why categorical data needs its own statistical tools.
Statistical Tests 10 min read

Statistical power is why your A/B test found nothing

The false negative problem that most analysts ignore. What statistical power means, how to calculate required sample sizes before you run an experiment, and why 'no significant effect' often just means 'inconclusive test'.
Statistical Tests 10 min read

ANOVA is not just multiple t-tests (and here's why)

Why running three t-tests on three groups gives you a 14% false positive rate instead of 5%. How ANOVA tests all groups simultaneously with one F-statistic, and when to use post-hoc comparisons.
Statistical Tests 10 min read

p-values are not what you were taught

The most misused number in science. What a p-value actually measures, what it cannot tell you, why 0.05 is arbitrary, and how p-hacking turns null results into publications.
View all posts