Visual Analytics Detect the Expected Discover the Unexpected A Tutorial for Middle School and High School Teachers Module 3- Misinformation and Lying with Graphics 1

Tutorial Outline Introduction Module 1What is Visual Analytics? Module 2Proper Visualization Construction and Use Module 3 Misinformation and Lying with Graphics Module 4Department of Homeland Security (DHS): Why and How They Use Visualizations Module 5Understandable Applications Module 6Exercises and Resources for the Classroom Conclusion 2

Module 3 – Misinformation and Lying with Graphics Statistics Statistical Sampling Historical Examples Problems with Data Collection Using a Critical Eye 3

Lies, Damned Lies, and Statistics Statistics Add Legitimacy Statistics Crunch Numbers Statistics Don’t Discriminate Statistics Run on Bad Data=> Statistics of Bad Data 4

Bad Data What is Bad Data? Why Does Bad Data Abound? Why Don’t We Get Good Data? Where Does Bad Data Come From? 5

People Collect Data Bad Data Comes From Us! Data Collection is a Social Activity Data is a Product of Choices, Compromises, Expertise, Attitude, Proximity 6

Algebra vs. Statistics Algebra Used to find answer 7+x=3 X=-4 One variable /one data point One answer Statistics Used to analyze data Corpus of data Characteristics of data 7

The Random Sample Random Selection from Entire Universe Set of Samples Representative of Universe Expensive & Difficult 8

Stratified Random Sample Universe Broken Down to Proportional Groups How Do You Know the Right Size of the Groups? Which Came First, Chicken or the Egg? Sampling Bias 9

Bane of Polling Organizations Techniques Evolved Over Time – More accurate The Poll That Changed Polling 10

1936 Presidential Election - Sampling Bias American Literary Digest Poll Based Off of Subscribers, Car Owners, and Phone Users Predicted Alf Landon Would Become the Next President of the U.S.A. 11

...But Roosevelt Won Re-election! What Happened? Sampling Bias! Over Represented Rich & Prosperous & Therefore Most Likely to Vote Republican Collecting (Good) Data is Hard and Expensive 12

President Dewey – Poor Data Collection Close Race Gallup Stopped Polling 2 Weeks Prior Prejudice 13

Data Collection Problems Sampling Bias Changing Opinions Bias Toward Own Opinions 14

Data Collection Problems Who is Asking? How Can I Answer? What Are They Asking? Public or Private Discourse? 15

Data Collection Problems: Who is Asking? Post WWII Study Would Blacks Be Better Off if Japan/ Germany Won the War? Response to White Questioner Response to Black Questioner 16

Data Collection Problems: How Can I Answer? Essay Multiple Choice Verbal Response 17

Data Collection Problems: What Are They Asking? Sensitive Issue Controversial Issue Unknown Issue 18

Data Collecting Problems: Public or Private Discourse? Public: Ordering at the Restaurant Same item Satisfaction Private: Secret Ballot Privacy Prevent bribery, intimidation 19

Data Collection - Why Do We Care? Poor/Bad Collection Affects Results Methodologies Being Developed To Remove Outside Influences Human - Psychological Machine - Calibration 20

Blind Experiments Single Blind Subject blind – experimenter not blind Pepsi Challenge Double Blind Subject & experimenter blind Placebo Group 21

Conclusion Results: Often Missing How Data Was Obtained Data Collection & Statistics Not Trivial Tasks Good Data Hard to Obtain Analyze Data with Critical Eye 22

Additional Resources al_election,_ al_election,_1936 How to Lie with Statistics – Darrel Huff Damned Lies and Statistics – Joel Best More Damned Lies and Statistics – Joel Best Predictably Irrational – Daniel Arielly Freakonomics – Dubner & Levitt Books by Malcolm Gladwell 23

Classroom Exercises Simulate an Open Ballot, Secret Ballot Analyze a Product Review, Endorsement Analyze Statistical Data for Lies Examine Demographics 24

Tutorial Outline Introduction Module 1What is Visual Analytics? Module 2Proper Visualization Construction and Use Module 3Misinformation and Lying with Graphics Module 4 Department of Homeland Security (DHS):Why and How They Use Visualizations Module 5Understandable Applications Module 6Exercises and Resources for the Classroom Conclusion 25