Statistics & Probability
The statistical reasoning behind experimentation and model evaluation.
Subject: Data & Statistics · Roles: AI Engineer, Business Intelligence Developer, Data Scientist, Machine Learning Engineer
Concepts
Probability Fundamentals
Events, conditional probability, and Bayes' theorem — the building blocks every statistical method assumes.
Probability Distributions
The handful of named distributions (normal, binomial, Poisson) that show up repeatedly, and what each models.
Hypothesis Testing
The framework for deciding whether an observed effect is likely real or could plausibly be noise — null hypotheses, p-values, and the two ways a test can be wrong.
A/B Testing
Applying hypothesis testing to compare two product/design variants — sample size, statistical power, and the traps of stopping early.
Regression Analysis
Modeling a relationship between variables — linear regression's assumptions, and what R² does and doesn't tell you.