This book will equip readers with the tools to navigate the mismatch of expectations. It is not intended to replace more technical applications of statistics but is accessible to those interested in learning about how poll data should be understood.
This book will equip readers with the tools to navigate the mismatch of expectations. It is not intended to replace more technical applications of statistics but is accessible to those interested in learning about how poll data should be understood.
Carl Allen is a researcher and analyst of sports and political data. His background includes quantitative analysis for University of Louisville Baseball, as well as MLB data for Stats Perform. Currently, Carl is the owner of Triple Digit Speed Pitch, a company that partners with schools and youth sports organizations for sports-themed fundraising events in Ohio. Carl's current research interests include inefficiencies in political betting markets, and improving polling methods. When he's not analyzing political data, Carl teaches English to adults, and he still finds time for MLB and NFL analysis. Carl received his BS from the University of Louisville in 2012 (Exercise Physiology, Spanish) and MS in 2013 (Sport Administration). You can find Carl on social media and Substack: @RealCarlAllen.
Inhaltsangabe
Preface 1. Public Consumption of Data: Some Historical Perspective 2. Polls 3. What Makes a Poll "Wrong" Part 1 4. Introducing: Ideal Polls 5. Throw it in the Average 6. What Makes a Poll "Wrong" Part 2 7. Introducing: Simultaneous Census, Present Polls, and Plan Polls 8. What's For Lunch? 9. The Fallacy of Margin (Spread) Analysis 10. The Fallacy of Proportion Analysis 11. Instrument Error: Weighted Results and Literal Weight 12. What's (actually) For Lunch? 13. Real Polls + Bad Math = Fake Errors 14. My Simultaneous Census 15. Introducing: Adjusted Poll Values 16. Compensating Errors and Poll Masking 17. Welcome to Mintucky 18. Mintucky Results 19. Mintucky Poll Error & Jacob Bernoulli 20. The Point Spread Problem 21. Remembering Nick Panagakis (1937-2018) 22. Finding the Base of Support 23. Trump-Clinton 2016 24. The Law of 50% + 1 25. The Clintons' Lessons 26. Trump-Clinton-3rd Party 2016 27. We* Don't Talk About Utah 28. Informing A Prediction 29. One Number Can Tell You A Lot 30. Don't Call It a "Rule" & How To Report Polls 31. UK Elections & Brexit 32. The Polls Weren't Wrong About Brexit 33. UK General Election Polls: Not Wrong Either 34. The Future, and "Try It"
Preface 1. Public Consumption of Data: Some Historical Perspective 2. Polls 3. What Makes a Poll "Wrong" Part 1 4. Introducing: Ideal Polls 5. Throw it in the Average 6. What Makes a Poll "Wrong" Part 2 7. Introducing: Simultaneous Census, Present Polls, and Plan Polls 8. What's For Lunch? 9. The Fallacy of Margin (Spread) Analysis 10. The Fallacy of Proportion Analysis 11. Instrument Error: Weighted Results and Literal Weight 12. What's (actually) For Lunch? 13. Real Polls + Bad Math = Fake Errors 14. My Simultaneous Census 15. Introducing: Adjusted Poll Values 16. Compensating Errors and Poll Masking 17. Welcome to Mintucky 18. Mintucky Results 19. Mintucky Poll Error & Jacob Bernoulli 20. The Point Spread Problem 21. Remembering Nick Panagakis (1937-2018) 22. Finding the Base of Support 23. Trump-Clinton 2016 24. The Law of 50% + 1 25. The Clintons' Lessons 26. Trump-Clinton-3rd Party 2016 27. We* Don't Talk About Utah 28. Informing A Prediction 29. One Number Can Tell You A Lot 30. Don't Call It a "Rule" & How To Report Polls 31. UK Elections & Brexit 32. The Polls Weren't Wrong About Brexit 33. UK General Election Polls: Not Wrong Either 34. The Future, and "Try It"
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