MegaMaester

Statistics for Everyday Life · Module 6

The Ideas of Statistics

The first five modules built the tools and habits of statistics. This module steps back to the ideas beneath them: where statistics came from, how it thinks, the studies that changed the world, and the debates that keep it honest.

You will explore the story of statistics, the frequentist and Bayesian schools of thought, landmark studies, how we establish cause, the replication crisis, the meeting of statistics and machine learning, and the statistical mind that ties it all together.

Lessons
7 lessons
Estimated time
~6-8 hours
Assessment
Module quiz included

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Lessons

Lesson 1 · 16 min

The Story of Statistics

Lesson 2 · 16 min

Frequentist and Bayesian Thinking

Lesson 3 · 16 min

Landmark Studies That Changed the World

Lesson 4 · 16 min

Causal Inference: From Correlation to Cause

Lesson 5 · 16 min

P-Hacking and the Replication Crisis

Lesson 6 · 16 min

Where Statistics Meets Machine Learning

Lesson 7 · 16 min

The Statistical Mind

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The Ideas of Statistics — Module Assessment

14 questions · pass mark 75%

  1. 1.What two historical activities are usually described as the early roots of statistics?
  2. 2.What is Adolphe Quetelet best known for contributing in the nineteenth century?
  3. 3.In the cookie example (Bowl 1: 30 vanilla, 10 chocolate; Bowl 2: 20 vanilla, 20 chocolate), you draw a vanilla cookie. What is the probability it came from Bowl 1?
  4. 4.Which statement best captures the difference between the frequentist and Bayesian schools?
  5. 5.Which design starts with healthy people and follows them forward in time to see who develops a disease?
  6. 6.Why did the apparent heart benefit of hormone replacement therapy largely disappear when tested in a randomized trial?
  7. 7.Why is random assignment considered the strongest way to establish a causal effect?
  8. 8.What best describes a natural experiment?
  9. 9.A study reports p = 0.02. Which statement is correct?
  10. 10.What is 'p-hacking'?
  11. 11.In Breiman's 'Two Cultures', what defines the algorithmic-modeling culture?
  12. 12.Why do machine-learning practitioners evaluate models on a held-out test set?
  13. 13.A test is described as "95 percent accurate" for a condition that affects 1 in 1,000 people. Someone tests positive. What does the statistical mind conclude?
  14. 14.Which statement best captures how a statistical mind treats uncertainty?
Answer every question to submit.