Lies, Damned Lies and Statistics @ PyCon Italia 2018

Statistics show that eating ice cream causes death by drowning.

If this sounds baffling, this talk will help you to understand correlation, bias, statistical significance and other statistical techniques that are commonly (mis)used to support an argument that leads, by accident or on purpose, to drawing the wrong conclusions.

The casual observer is exposed to the use of statistics and probability in everyday life, but it is extremely easy to fall victim of a statistical fallacy, even for professional users.

The purpose of this talk is to help the audience understand how to recognise and avoid these fallacies, by combining an introduction to statistics with examples of lies and damned lies, in a way that is approachable for beginners.

Transcript

1. Lies, Damned Lies
 and Statistics @MarcoBonzanini PyCon Nove Florence, Italy April 2018
2. In the Vatican City
 there are 5.88 popes
 per square mile 2
3. This talk is about: • The misuse of statistics in everyday life • How (not) to lie with statistics This talk is not about: • Python • Advanced Statistical Models The audience (you!): • Good citizens • An interest in statistical literacy
 (without an advanced Math degree?) 3
4. LIES, DAMNED LIES
 AND CORRELATION
5. Correlation 5
6. Correlation • Informal: a connection between two things • Measure the strength of the association between two variables 6
7. Linear Correlation 7
8. Linear Correlation 8 Positive Negative
9. Correlation Example 9
10. Correlation Example 10 Temperature Ice Cream
 Sales ($$$)
11. “Correlation 
 does not imply
 causation” 11
12. 12 Deaths by
 drowning Ice Cream
 Sales ($$$)
13. 13 Lurking Variable
14. 14 Temperature Ice Cream
 Sales ($$$) Temperature Deaths by
 drowning Lurking Variable
15. More Lurking Variables 15
16. More Lurking Variables 16 Damage
 caused
 by fire Firefighters
 deployed
17. More Lurking Variables 17 Damage
 caused
 by fire Firefighters
 deployed Fire severity?
18. Correlation and causation 18
19. Correlation and causation • A causes B, or B causes A • A and B both cause C • C causes A and B • A causes C, and C causes B • No connection between A and B 19
20. 20 http://www.tylervigen.com/spurious-correlations
21. 21 http://www.tylervigen.com/spurious-correlations
22. 22 https://www.buzzfeed.com/kjh2110/the-10-most-bizarre-correlations
23. 23 https://www.buzzfeed.com/kjh2110/the-10-most-bizarre-correlations
24. 24 http://www.nejm.org/doi/full/10.1056/NEJMon1211064
25. LIES, DAMNED LIES,
 SLICING AND DICING
 YOUR DATA
26. Simpson’s
 Paradox 26
27. 27 University of California, Berkeley Graduate school admissions in 1973 https://en.wikipedia.org/wiki/Simpson%27s_paradox
28. 28 University of California, Berkeley Graduate school admissions in 1973 https://en.wikipedia.org/wiki/Simpson%27s_paradox Gender bias?
29. 29 University of California, Berkeley Graduate school admissions in 1973 https://en.wikipedia.org/wiki/Simpson%27s_paradox
30. 30 University of California, Berkeley Graduate school admissions in 1973 https://en.wikipedia.org/wiki/Simpson%27s_paradox
31. 31 University of California, Berkeley Graduate school admissions in 1973 https://en.wikipedia.org/wiki/Simpson%27s_paradox
32. 32 University of California, Berkeley Graduate school admissions in 1973 https://en.wikipedia.org/wiki/Simpson%27s_paradox
33. LIES, DAMNED LIES
 AND SAMPLING BIAS
34. Sampling 34
35. Sampling 35 • A selection of a subset of individuals • Purpose: estimate about the whole population • Hello Big Data!
36. Bias 36
37. Bias 37 • Prejudice? Intuition? • Cultural context? • In science: a systematic error
38. “Dewey defeats Truman” 38
39. “Dewey defeats Truman” 39 https://en.wikipedia.org/wiki/Dewey_Defeats_Truman
40. “Dewey defeats Truman” 40 https://en.wikipedia.org/wiki/Dewey_Defeats_Truman • The Chicago Tribune printed the wrong headline on election night • The editor trusted the results of the phone survey • … in 1948, a sample of phone users was not representative of the general population
41. Survivorship Bias 41
42. Survivorship Bias • Bill Gates, Steve Jobs, Mark Zuckerberg
 are all college drop-outs • … should you quit studying? 42
43. LIES, DAMNED LIES
 AND DATAVIZ
44. “A picture is worth a thousand words” 44
45. 45 https://en.wikipedia.org/wiki/Anscombe%27s_quartet
46. 46 https://venngage.com/blog/misleading-graphs/
47. 47 https://venngage.com/blog/misleading-graphs/
48. 48 https://venngage.com/blog/misleading-graphs/
49. 49 http://www.businessinsider.com/gun-deaths-in-florida-increased-with-stand-your-ground-2014-2?IR=T
50. 50 http://www.businessinsider.com/gun-deaths-in-florida-increased-with-stand-your-ground-2014-2?IR=T
51. 51 http://www.businessinsider.com/gun-deaths-in-florida-increased-with-stand-your-ground-2014-2?IR=T
52. 52 https://www.raiplay.it/video/2016/04/Agor224-del-08042016-4d84cebb-472c-442c-82e0-df25c7e4d0ce.html
53. 53 https://www.theguardian.com/news/datablog/2014/may/12/lies-election-leaflets-five-tricks-european-elections
54. 54 https://www.theguardian.com/news/datablog/2014/may/12/lies-election-leaflets-five-tricks-european-elections
55. 55 https://www.theguardian.com/news/datablog/2014/may/12/lies-election-leaflets-five-tricks-european-elections
56. 56 https://www.theguardian.com/news/datablog/2014/may/12/lies-election-leaflets-five-tricks-european-elections
57. LIES, DAMNED LIES
 AND SIGNIFICANCE
58. Significant = Important 58 ?
59. Statistically Significant Results 59
60. Statistically Significant Results 60 • We are quite sure they are reliable (not by chance) • Maybe they’re not “big” • Maybe they’re not important • Maybe they’re not useful for decision making
61. p-values 61
62. 62 https://en.wikipedia.org/wiki/Misunderstandings_of_p-values
63. p-values • Probability of observing our results (or more extreme) when the null hypothesis is true • Probability, not certainty • Often p < 0.05 (arbitrary) • Can we afford to be fooled by randomness
 every 1 time out of 20? 63
64. Data dredging 64
66. Data dredging • a.k.a. Data fishing or p-hacking • Convention: formulate hypothesis, collect data, prove/disprove hypothesis • Data dredging: look for patterns until something statistically significant comes up • Looking for patterns is ok
 Testing the hypothesis on the same data set is not 66
67. SUMMARY
68. — Dr. House “Everybody lies” 68
69. 69 • Good Science ™ vs. Big headlines • Nobody is immune • Ask questions: What is the context? Who’s paying? What’s missing? • … “so what?”
70. THANK YOU @MarcoBonzanini speakerdeck.com/marcobonzanini GitHub.com/bonzanini marcobonzanini.com