The Statistics You Actually Need for Data Science

You don't need a graduate degree in statistics to be useful with data. You need a tight set of concepts you truly understand and can apply.

The 12 essentials

• Mean, median, mode — and when each one lies to you.
• Variance and standard deviation — measuring spread.
• Distributions: normal, log-normal, Poisson, power-law.
• Central Limit Theorem — why sample means behave nicely.
• Sampling and sampling bias — the silent killer of analyses.
• Confidence intervals — uncertainty made explicit.
• Hypothesis testing — t-tests, chi-square, and how to read a p-value.
• A/B testing — the applied version of hypothesis testing.
• Correlation vs causation — the eternal warning.
• Linear regression — the foundation of most predictive modeling.
• Logistic regression — and the meaning of odds ratios.
• Bayes' theorem — updating beliefs as new data arrives.

A common pitfall

A p-value of 0.04 doesn't mean 'there's a 96% chance the effect is real'. It means: 'if there were no effect, we'd see data this extreme 4% of the time'. Mis-reading p-values causes more bad business decisions than almost any other statistical mistake.

How to study these

Pick one per week. Write a 5-line notebook that demonstrates it on real or simulated data. That's it. Twelve weeks, twelve notebooks, and you'll have a working statistical intuition most engineers never develop.