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Statistics for Everyday Life · Module 7

The Statistician's Toolkit

Earlier modules built the ideas and habits of statistics. This module is a practical toolkit: the handful of methods you meet again and again in studies, reports, and the news — the t-test, chi-square, regression, ANOVA, effect sizes and confidence intervals, and robust and nonparametric methods.

Everything here is explained in plain language, without heavy formulas. The goal is to understand what each method asks, when to use it, and how to read its results critically — so you can make sense of the statistics that shape decisions all around you.

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

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Lessons

Lesson 1 · 16 min

Comparing Two Groups: The t-Test

Lesson 2 · 16 min

Categorical Data: The Chi-Square Test

Lesson 3 · 16 min

Explaining and Predicting with Regression

Lesson 4 · 16 min

Comparing Many Groups: ANOVA

Lesson 5 · 16 min

Beyond p-values: Effect Sizes and Intervals

Lesson 6 · 16 min

When Data Breaks the Rules: Robust and Nonparametric Methods

Lesson 7 · 16 min

Choosing the Right Method

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The Statistician's Toolkit — Module Assessment

14 questions · pass mark 75%

  1. 1.A t-test primarily answers the question:
  2. 2.A very large sample can make a t-test result 'statistically significant' even when the difference is:
  3. 3.The chi-square test is designed for:
  4. 4.A significant chi-square result tells you there is an association, but not:
  5. 5.Compared with a simple correlation, multiple regression additionally lets you:
  6. 6.'Regression to the mean' refers to the tendency for:
  7. 7.Why not just run a separate t-test on every pair of many groups?
  8. 8.ANOVA works by comparing:
  9. 9.An effect size, unlike a p-value, tells you:
  10. 10.The American Statistical Association's 2016 statement warned that a p-value:
  11. 11.Rank-based nonparametric tests are especially useful when data:
  12. 12.When the standard assumptions roughly hold, classic tests (t-test, ANOVA) are often:
  13. 13.The best starting point for choosing a statistical method is:
  14. 14.Choosing the correct test is only 'half the job' because:
Answer every question to submit.