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Why are Polls So Wrong? CTC1-1A 4 Dec, 2016 1A 1A 2016 Schield - PDF document

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Why are Polls So Wrong? CTC1-1A 4 Dec, 2016 1A 1A 2016 Schield CTC1 1 2016 Schield CTC1 2 2016 US Presidential Election 2016 Aug 12 Trump 12% Why Are Polls So Wrong? Nate Silver 538 . Milo Schield Augsburg College Minneapolis

  1. Why are Polls So Wrong? CTC1-1A 4 Dec, 2016 1A 1A 2016 Schield CTC1 1 2016 Schield CTC1 2 2016 US Presidential Election 2016 Aug 12 Trump 12% Why Are Polls So Wrong? Nate Silver 538 . Milo Schield Augsburg College Minneapolis Critical Thinking Club Copy of these slides at: www.StatLit.org/pdf/2016-Schield-CTC1-Slides.pdf 1A 1A 2016 Schield CTC1 3 2016 Schield CTC1 4 2016 Nov 5 Trump 35% 2015 Oct 22 Trump 14% Nate Silver 538 Nate Silver 538 . . 1A 1A 2016 Schield CTC1 5 2016 Schield CTC1 6 Final Tally How Far Off? . . 2016-Schield-CTC1-Slides.pdf 1

  2. Why are Polls So Wrong? CTC1-1A 4 Dec, 2016 1A 1A 2016 Schield CTC1 7 2016 Schield CTC1 8 Blame the Polls Why the anti-Trump bias? . Some Plausible Explanations: 1. Pollsters are liberals or have a liberal bias. 2. Ignored statistical margin of error (plus/minus 3 points) 3. Ignored correlation between state margins. 4. Big “Undecided” up to election day. [Allocated 50/50] 5. Ignored time. Predicted using static analysis. 6. Actual election-prediction error 7. Selection bias by everyone. 1A 1A 2016 Schield CTC1 9 2016 Schield CTC1 10 2a Not Statistically Significant 1. Liberal Bias Popular Vote: 11/05 . . 1A 1A 2016 Schield CTC1 11 2016 Schield CTC1 12 2b Not Statistically Significant 3. State Margins: Correlated Electoral Votes: 11/05 . OH-MI: 0.90 OH-WI: 0.86 MI-WI: 0.86 IA-WI: 0.84 IA-OH: 0.84 IA-MI: 0.83 MN-OH: 0.81 MN-WI: 0.80 MN-MI: 0.79 PA-MN: 0.72 2016-Schield-CTC1-Slides.pdf 2

  3. Why are Polls So Wrong? CTC1-1A 4 Dec, 2016 1A 1A 2016 Schield CTC1 13 2016 Schield CTC1 14 4. Undecided: Twice as big 4. Undecided… Already Decided for Trump . Some "undecided" voters had already decided in favor of Trump, but didn't want to admit it. Polls normally split • Some polls showed Trump getting 0% of undecided 50-50 the black vote in Pennsylvania and Ohio. • In exit polls, Trump got about 8% of the black vote. . 1A 1A 2016 Schield CTC1 15 2016 Schield CTC1 16 5. Late-Breaking “Change” 4. Last-Week Deciders Ignored time Voters who said they were “undecided” until the election (last-week deciders) typically voted for Trump. And they did so – by big margins! . Should have “predicted” next week for each state. 1A 1A 2016 Schield CTC1 17 2016 Schield CTC1 18 6. Average of all Polls 6. Average of all National Polls Is Not Very Accurate Is Not Very Accurate . . 95% Margin of Error: 3.6 Points 2016-Schield-CTC1-Slides.pdf 3

  4. Why are Polls So Wrong? CTC1-1A 4 Dec, 2016 1A 1A 2016 Schield CTC1 19 2016 Schield CTC1 20 6. State Error is Typically 6. Election Polls are More than National Error more ‘Art” than ‘Science” . 95% Margin of Error: 4.8 Points We gave some good pollsters the same data. They gave very different results!!! 1A 1A 2016 Schield CTC1 21 2016 Schield CTC1 22 7. Selection Bias. Conclusion National Polls were OK Nate Silver (11/08) predicted a 3.6 point margin for Hillary: Election polls are closer to fortune telling to facts. • Clinton: 48.5% Johnson 5.0% Election polls are different (very different) from surveys! • Trump: 44.9% Other: 0.6% Surveys report! Election polls predict. http://projects.fivethirtyeight.com/2016-election-forecast/?ex_cid=rrpromo Surveys never (almost) adjust. In fact, Hillary “won” by at least a 1.3 point margin: Election polls always adjust • Clinton: 48.0% Other: 5.3% • Trump: 46.7% Polls have to adjust • to match the profile of those that will vote. http://cookpolitical.com/story/10174 Readers are guilty of selection bias; • for (how to allocate) the undecided. • Inferring Electoral-College win from Popular-Vote win. • the non-response bias. 1A 1A 2016 Schield CTC1 23 2016 Schield CTC1 24 Best Predictor? Conclusion Halloween Mask Sales . www.bloomberg.com/news/videos/2016-10-13/can-halloween-masks-predict-the-winner-of-the-election . 2016-Schield-CTC1-Slides.pdf 4

  5. 1A 2016 Schield CTC1 1 2016 US Presidential Election Why Are Polls So Wrong? Milo Schield Augsburg College Minneapolis Critical Thinking Club Copy of these slides at: www.StatLit.org/pdf/2016-Schield-CTC1-Slides.pdf

  6. 1A 2016 Schield CTC1 2 2016 Aug 12 Trump 12% Nate Silver 538 .

  7. 1A 2016 Schield CTC1 3 2015 Oct 22 Trump 14% Nate Silver 538 .

  8. 1A 2016 Schield CTC1 4 2016 Nov 5 Trump 35% Nate Silver 538 .

  9. 1A 2016 Schield CTC1 5 Final Tally .

  10. 1A 2016 Schield CTC1 6 How Far Off? .

  11. 1A 2016 Schield CTC1 7 Blame the Polls .

  12. 1A 2016 Schield CTC1 8 Why the anti-Trump bias? Some Plausible Explanations: 1. Pollsters are liberals or have a liberal bias. 2. Ignored statistical margin of error (plus/minus 3 points) 3. Ignored correlation between state margins. 4. Big “Undecided” up to election day. [Allocated 50/50] 5. Ignored time. Predicted using static analysis. 6. Actual election-prediction error 7. Selection bias by everyone.

  13. 1A 2016 Schield CTC1 9 1. Liberal Bias .

  14. 1A 2016 Schield CTC1 10 2a Not Statistically Significant Popular Vote: 11/05 .

  15. 1A 2016 Schield CTC1 11 2b Not Statistically Significant Electoral Votes: 11/05 .

  16. 1A 2016 Schield CTC1 12 3. State Margins: Correlated OH-MI: 0.90 OH-WI: 0.86 MI-WI: 0.86 IA-WI: 0.84 IA-OH: 0.84 IA-MI: 0.83 MN-OH: 0.81 MN-WI: 0.80 MN-MI: 0.79 PA-MN: 0.72

  17. 1A 2016 Schield CTC1 13 4. Undecided: Twice as big . Polls normally split undecided 50-50

  18. 1A 2016 Schield CTC1 14 4. Undecided… Already Decided for Trump Some "undecided" voters had already decided in favor of Trump, but didn't want to admit it. • Some polls showed Trump getting 0% of the black vote in Pennsylvania and Ohio. • In exit polls, Trump got about 8% of the black vote. .

  19. 1A 2016 Schield CTC1 15 4. Last-Week Deciders Voters who said they were “undecided” until the election (last-week deciders) typically voted for Trump. And they did so – by big margins! .

  20. 1A 2016 Schield CTC1 16 5. Late-Breaking “Change” Ignored time Should have “predicted” next week for each state.

  21. 1A 2016 Schield CTC1 17 6. Average of all Polls Is Not Very Accurate .

  22. 1A 2016 Schield CTC1 18 6. Average of all National Polls Is Not Very Accurate . 95% Margin of Error: 3.6 Points

  23. 1A 2016 Schield CTC1 19 6. State Error is Typically More than National Error . 95% Margin of Error: 4.8 Points

  24. 1A 2016 Schield CTC1 20 6. Election Polls are more ‘Art” than ‘Science” We gave some good pollsters the same data. They gave very different results!!!

  25. 1A 2016 Schield CTC1 21 7. Selection Bias. National Polls were OK Nate Silver (11/08) predicted a 3.6 point margin for Hillary: • Clinton: 48.5% Johnson 5.0% • Trump: 44.9% Other: 0.6% http://projects.fivethirtyeight.com/2016-election-forecast/?ex_cid=rrpromo In fact, Hillary “won” by at least a 1.3 point margin: • Clinton: 48.0% Other: 5.3% • Trump: 46.7% http://cookpolitical.com/story/10174 Readers are guilty of selection bias; • Inferring Electoral-College win from Popular-Vote win.

  26. 1A 2016 Schield CTC1 22 Conclusion Election polls are closer to fortune telling to facts. Election polls are different (very different) from surveys! Surveys report! Election polls predict. Surveys never (almost) adjust. Election polls always adjust Polls have to adjust • to match the profile of those that will vote. • for (how to allocate) the undecided. • the non-response bias.

  27. 1A 2016 Schield CTC1 23 Conclusion .

  28. 1A 2016 Schield CTC1 24 Best Predictor? Halloween Mask Sales www.bloomberg.com/news/videos/2016-10-13/can-halloween-masks-predict-the-winner-of-the-election .

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