Data science

Stage 2 · Lesson 8 of 8

Correlation, confounders and causation

Statistics and probability you actually use

Correlation measures how two variables move together, from minus one to one. correlation captures straight-line relationships; Spearman works on ranks and handles curves and better. Correlation is not causation: a confounder, a third factor driving both variables, can create a strong link with no cause behind it.

Heavy shoppers use wishlists and spend more, but the wishlist may not cause the spending. To claim a cause you need an experiment, which the experiments stage covers, or a careful design that rules out confounders. Always plot a scatter chart before quoting a correlation.

Common traprecommending a business change because two numbers move together, then seeing nothing happen when the change ships.

A question to check yourself, an AI practice session and 1 cheat sheet entry come with the full roadmap. Unlock

Resources for this stage

Start with· follow one of these

  • VideoFree
    StatQuest with Josh Starmer

    StatQuest · Beginner

    Free: short, clear videos on each concept, from the normal distribution to p-values and power

  • CourseFree
    Khan Academy Statistics and Probability

    Khan Academy · Beginner

    Free: the probability, sampling distributions, confidence intervals and significance tests units, with practice

Practise

  • Free textbook with end-of-chapter exercises on real data, and labs in R

  • Free: statistics taught through Python code on real datasets, with notebooks you can run

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