What's Next?

Continue Your Statistics Journey

Deeper Statistics

  • ANOVA — compare means across three or more groups simultaneously.
  • Multiple regression — model y as a linear combination of multiple predictors using statsmodels.
  • Bayesian inference — quantify beliefs with prior distributions and update with data using PyMC or Stan.
  • Non-parametric tests — Mann-Whitney U, Wilcoxon signed-rank for when normality can't be assumed.

Visualization

  • Matplotlib — histograms, box plots, scatter plots with regression lines, QQ-plots for normality.
  • Seaborn — statistical visualization with built-in distribution plots, pair plots, and heatmaps.

Build Something

  • A/B test simulator — generate two conversion rate datasets, run a two-sample t-test, and estimate the sample size needed for statistical power.
  • Distribution explorer — interactive sliders for μ and σ, showing how the normal distribution PDF and CDF change.
  • Bootstrap vs. t-test comparison — verify that bootstrap CIs and t-test CIs agree for normally distributed data.

References

  • Statistics by Freedman, Pisani & Purves — the most intuitive introduction to statistical reasoning.
  • Think Stats by Allen Downey — statistics with Python, free online.
  • SciPy stats documentation — full reference for all distributions and tests.
  • StatQuest with Josh Starmer — YouTube channel with clear visual explanations of every concept in this course.
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