Statistics & Probability
A/B testing, hypothesis testing, Bayesian reasoning — the statistical bedrock of ML engineering.
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- Explain Bayesian vs. frequentist thinking — when does each matter in ML?medium
- Explain p-values, Type I/II errors, and statistical power to a non-statisticianeasy
- Which probability distributions matter most in ML, and when do you use each?medium
- Your dataset has sampling bias — how do you detect and correct it?hard
- Why does the Central Limit Theorem matter for ML and statistics?Newmedium
- What is maximum likelihood estimation and why is it everywhere in ML?Newmedium
- What is the multiple testing problem and how do you control for it?Newhard
- What is Simpson's paradox and when does it trap ML practitioners?Newmedium
- How does causal inference differ from correlation-based ML, and when does it matter?Newhard
- What is bootstrapping and when should you use it over analytical methods?Newmedium
- How do you determine the sample size needed for an A/B test?Newhard
- Effect size vs statistical significance — why significant results can be meaninglessNewmedium