MegaMaester

Statistics for Everyday Life · Module 4

Working with Data

The first three modules built the ideas of statistics: describing data, judging evidence, and reasoning about probability. This module is the hands-on counterpart — the real workflow of turning messy data into trustworthy answers.

You will learn the end-to-end analysis workflow, how to clean and prepare data, how to explore a dataset before concluding, how to compare groups and run A/B tests, how to build and test simple predictions, how to communicate findings honestly, and what it means to be data-literate.

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

Not started yet

Lessons

Lesson 1 · 16 min

The Data Analysis Workflow

Lesson 2 · 16 min

Cleaning and Preparing Data

Lesson 3 · 16 min

Exploratory Data Analysis

Lesson 4 · 16 min

Comparing Groups and A/B Testing

Lesson 5 · 16 min

From Data to Predictions

Lesson 6 · 16 min

Communicating with Data

Lesson 7 · 16 min

Becoming Data-Literate

Check your understanding

Finish the lessons, then test yourself. Your best score is saved on this device. You can also open this quiz on its own page.

Working with Data — Module Assessment

14 questions · pass mark 75%

  1. 1.According to the workflow, what should happen before you choose data or methods?
  2. 2.Industry surveys such as the 2016 CrowdFlower report and a 2014 New York Times piece suggest that data professionals spend most of their time on what?
  3. 3.Why can dropping every row that has a missing value distort your analysis?
  4. 4.In the 2013 critique of Reinhart and Rogoff's 2010 paper, what did Thomas Herndon and colleagues find?
  5. 5.What is the central lesson of Anscombe's quartet?
  6. 6.A dataset of website visit times has an average of four minutes, but almost no visitor stays near four minutes. What is the most likely explanation an EDA plot would reveal?
  7. 7.Why is random assignment central to a fair A/B test?
  8. 8.An experiment on ten million users finds a new button color raises clicks by 0.001 percent, and the result is highly statistically significant. What is the right takeaway?
  9. 9.A model predicts last year's sales almost perfectly but does poorly on this year's. What has most likely happened?
  10. 10.Why did the Netflix Prize score final results on a set of ratings the teams never saw?
  11. 11.A bar chart makes a change from 10 to 9 look like a near-halving. What design choice most likely caused this?
  12. 12.What did Edward Tufte mean by 'chartjunk'?
  13. 13.According to this lesson, what best captures the core of being data-literate?
  14. 14.A promotional report claims a program "improved outcomes by 30 percent" with no baseline, sample size, or margin of error given. What is the most data-literate response?
Answer every question to submit.