1. Review Part 1 Cognitive Biases

2. PARTICIPATION AGAIN

3. Final Exam 17 December 2013 (Tuesday) 18:30-20:30 In the Gymnasium 20 Questions All short answer 5 marks each Worth 20% of the course grade

4. Deadlines Participation due: 10 th of December.

5. REGRESSION FALLACY

6. Pareidolia

7. Patterns People see patterns where there aren’t any. Some scientists think this is because it’s better to see too many patterns than too few, so our brains are designed to find patterns wherever possible.

8. Correlation Two variables are correlated when the value of one gives you (some) information about the value of the other.

9. Correlation (but not Causation)

10. Regression to the Mean Whenever two variables are imperfectly correlated, extreme values of one variable tend to be paired with less extreme values of the other.

11. Average of Parents Height # of Parents# of Children Shorter by 0.67 sd # of Children Taller by 0.67 sd Bottom 2%7014 2%-9%45232 9%-25%8019 Middle 50%219610 75%-91%50160 91%-98%14290 Top 2%41000

12. Average Phenomenon Regression is an average phenomenon. It doesn’t mean that any two tall parents will have children who are shorter than them. It means that the tallest parents will have children who are on average shorter than them.

13. Regression Fallacy The regression fallacy is when you attribute a cause to a regression effect. “We instituted new standards and pollution levels have been dropping since 2004!”

14. Regression Fallacy

15. WASON SELECTION TASK

16. Even vs. Odd Even numbers: 2, 4, 6, 8, 10, 12… Odd numbers: 1, 3, 5, 7, 9, 11, 13…

17. Wason Selection Task Suppose that I present you with four cards. On each card there is a number on one side and a color (blue or red) on the other. I claim: If a card has an even number on one side then it is blue on the other side. Which of the four cards do you need to turn over to tell whether this claim is true or false?

18. A B C D

19. Wason Selection Task Card A doesn’t matter. First possibility: 1. The other side is blue. The claim says if it’s even, then it’s blue. It does not say that if it’s not even, then it is not blue.

20. Example True claim: if a student makes an appointment, she can see me in my office. Does not mean false claim: if a student does not make an appointment, she cannot see me in my office.

21. Wason Selection Task Card A doesn’t matter. Second possibility: 2. The other side is red. The claim says even cards can’t be red. It does not say odd cards can’t be red.

22. A B C D

23. Wason Selection Task Card D doesn’t matter. Two possibilities: 1. The other side is even (for example, it’s “4”). The claim says if it’s even, then it’s blue. It does not say that if it’s blue, then it’s even.

24. Example True claim: If something is a dog, then it is an animal. Does not mean false claim: if something is an animal, then it is a dog.

25. Wason Selection Task Card D doesn’t matter. Two possibilities: 2. The other side is odd (for example, it’s “3”). The claim says if it’s even, then it’s blue. It does not say that if it’s not even, then it is not blue.

26. A B C D

27. Wason Selection Task Card B is important. 4 is an even number. If other side of card B is red, then the claim is false, because B is a card with an even number on one side but it is not blue on the other side. You must turn over B and make sure it is not red on the other side.

28. A B C D

29. Wason Selection Task Card C is also important. If the claim is true, this card must have an odd number on the other side. If it has an even number on the other side, then the claim is false. You must turn over #3 and make sure there is not an odd number on the other side.

30. Statistical Results Around ½ of people studied say “B: 4” and “D: Blue”. About 1/3 say just “B: 4”. Only about 1/20 get the right answer: “B: 4” and “C: Red”!

31. Analysis People look for results that would agree with the claim. Turning over B and D, you could get agreement– for example, [4, Blue] and [6, Blue]. You cannot get agreement by turning over C. But you can get disagreement and that is why the card is important!

33. CONFIRMATION BIAS

34. Confirmation Bias People tend to look for evidence that agrees with what they already believe. This is called confirmation bias.

35. CONFIRMATION/ DISCONFIRMATION BIAS

36. Confirmation Bias People tend to look for evidence that agrees with what they already believe. This is called confirmation bias.

37. U.S. Slavery

38. Slavery Apologist “No, I haven’t seen the [12 Years a Slave], but I’ve read reviews… A lot of people were very happy with [slavery/ oppression under communism]. You didn’t have to think much, or take much responsibility. And that suits many of us just fine.” – John Derbyshire

39. Seeking Confirmation: Sources

40. If you assume someone is untrustworthy, you usually don’t stick around to see what they say, and then later find out whether they were telling the truth or not. “People tend to grossly underestimate the trustworthiness of other people.” (Fechtenhauer & Dunning)

41. Disconfirmation Bias Disconfirmation bias is the tendency to subject evidence against your views to a greater degree of scrutiny than evidence in favor of your views. It is a double-standard for evidence evaluation.

42. “Republicans who think they understand the global warming issue best are least concerned about it; and among Republicans and those with higher levels of distrust of science in general, learning more about the issue doesn't increase one's concern about it.” – ChrisMooney

43. Dunning-Kruger The Dunning-Kruger effect is when people: (a)Perform badly because they lack the skills and knowledge for the task. (b)Think they are performing well because they lack the same skills and knowledge to evaluate themselves.

45. Motivated Reasoning "People who have a dislike of some policy—for example, abortion—if they're unsophisticated they can just reject it out of hand," says Lodge. "But if they're sophisticated, they can go one step further and start coming up with counterarguments.“ – Taber & Lodge

46. Climategate “In late November 2009, more than 1,000 emails between scientists at the Climate Research Unit of the U.K.’s University of East Anglia were stolen and made public by an as- yet-unnamed hacker. Climate skeptics are claiming that they show scientific misconduct that amounts to the complete fabrication of man-made global warming.” – FactCheck.org

47. The Smoking Gun “I’ve just completed Mike’s Nature trick of adding in the real temps to each series for the last 20 years (i.e., from 1981 onwards) and from 1961 for Keith’s to hide the decline.” Using tricks to hide the decline in average global temperature!

48. What the Researchers Did Observed increase in temperature since the 60’s. Tree-ring data correlate with rise in temperature until the 60’s. Then they show “decrease” when we actually observe increase. Trick: present real data alongside tree-ring data.

49. BASE RATE FALLACY

50. Perfect English 100% of Westerners write perfect English. Only 5% of Chinese/ Japanese/ Malaysian/ Indonesian/ Indian/ Pakistani/ Thai/ etc. write perfect English.

51. This OpenRice review is written in perfect English. What is the probability that the author is a Westerner? 100% 95% 12%

52. Base Rates Western population 0.7% Ethnic Chinese: 93% Total Non-Western: 99.3%

53. Demographics 5% of Non-Westerners write perfect English: So, 5% x 99.3% x 7.155m = 355,245 100% of Westerners write perfect English: So, 100% x 0.7% x 7.155m = 50,085

54. Probability Westerner wrote review: #Westerners with perfect English ÷ #People with perfect English

55. Probability Westerner wrote review: 50085 ÷ 405330 = 12%

56. Importance of Base Rates The proportion of false positives out of total positives increases: False Positive ÷ False Positive + True Positive

57. MULTIPLE ENDPOINTS

58. Multiple Endpoints One way that our expectations can influence our beliefs is called “the problem of multiple endpoints.”

59. Fortune Telling “Your daughter won’t succeed in applications to universities in the northwest and northeast, but she will succeed if she applies to universities in the southwest and southeast.”

60. Fortune Telling Obviously, there are lots of ways for this fortune to come true! (2,236 Universities in China) And, if it’s false, the fortune teller can always cite “other reasons” for the outcome. He can never really be wrong.

61. Multiple Endpoints/ Comparisons

62. Dead Fish Craig Bennett is a neuroscience graduate student. He wanted to test out his fMRI machine, so he bought a whole dead salmon. He put the dead salmon in the machine and showed it “a series of photographs depicting human individuals in social situations.”

63. Experimental Design The salmon “was asked to determine what emotion the individual in the photo must have been experiencing.” Then Bennett looked to see whether there were correlations between changes in the blood flow in the salmon’s brain, and the pictures.

64. Correlations! Unsurprisingly, there were. 16 out of 8,064 voxels (volumetric pixels) were correlated with picture-viewing.

65. Multiple Endpoints If there are enough “goals” then there will always be some success. Low false positives will still result in many positives if you look broadly enough.