Review

1. Review

2. What is Critical Thinking? There are two basic decisions to make in life: 1. Decide what to believe: What do I believe? 2. Decide what to do: What do I do?

3. What Should I Believe? Is there any evidence to support the claim? Is the evidence reliable and trustworthy? How reliable is it? Should you accept it? Does the evidence actually support the claim? Is there other evidence you should consider?

4. What Should I Do? What outcomes can my choice lead to? Does the outcome of my decision depend on factors other than what I choose to do? What is the likelihood that deciding to take a specific action will lead to a specific outcome? Which outcomes do I most prefer?

5. context

6. The Importance of Context When someone provides you with evidence for a truth-claim, you have to ask: Does this evidence really support the claim? Is there other relevant evidence I should look for before assessing the claim?

7. Taking Quotes out of Context Every quote is taken out of its original context. That’s why it’s a quote and not a reproduction. This is fine if the person providing the quote has also provided enough context that the quote does not mislead you into thinking someone meant something they did not mean. Numbers, charts, and figures can be taken out of context as well, in misleading ways.

8. Trends and Truncated Charts When you see a chart describing what happened in the last 10 years, or last 10 months, or last 10 weeks, you’re seeing the information out of context. What happened before then? When the y-axis of a chart does not start at zero (a “zero baseline”), you’re seeing what’s called a “truncated”, “torn” or “gee whiz” graph. You’re seeing the information it presents out of context.

9. Misleading Figures Absolute numbers can be misleading. India has about 330,000 people in prison. A lot or a little? Rates can also be misleading. Russia has 615 prisoners for every 100,000 people. A lot or a little? Even comparisons can be misleading. If country X has twice as many people in prison today than in 1990, is that bad? What if they have twice as many residents than in 1990?

10. Cognitive biases

11. The Clustering Illusion Here is 20 random coin flips: XXXXOOXOOXXOOXOOXOOO That doesn’t look random. But it is. The coin lands the same as the previous toss 10 times and different from the previous toss 9 times.

12. Confabulation The human mind sees patterns where there is only randomness. It also freely invents reasons and explanations to “make the world make sense.” When we encounter random data, we see a pattern that isn’t there. And we explain why there should be a pattern. This can make our bad beliefs difficult to abandon.

13. 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. The regression fallacy involves attributing a causal explanation to what is nothing more than regression to the mean.

14. Confirmation Bias Even though evaluating predictions requires looking at both the rates of true positives among predicted positives, and the rates of false positives among predicted negatives, human beings have a tendency to only consider true positives (and to a lesser extent, true negatives) when evaluating predictions or other claims of imperfect correlation.

15. The Problem of Absent Data Sometimes it’s not just that we only look for or evaluate the positive evidence, but that there is no negative evidence. This can lead us to think we have very well-confirmed beliefs when we do not.

16. Bias Our expectations often influence how we evaluate claims and evidence. We easily accept as true those things that we expect to be true, but are much more skeptical about things that are unexpected.

17. 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.

18. Conjunction Fallacy Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations. Which is more probable? 1. Linda is a bank teller. 2. Linda is a bank teller and is active in the feminist movement.

19. Conjunction Fallacy Always, the probability of two events happening (Linda being a bank teller AND Linda being a feminist) is less than the probability of just one of those events happening (for example, Linda being a bank teller). The illusion that the opposite is true especially occurs in cases where one event explains the other.

20. Representativeness Our (false) judgment that Linda is more likely to be a feminist bank teller than to just be a bank teller is an example of how we judge the truth of claims based on how “representative” they are. The representativeness heuristic is partly responsible for other biases as well: “clusters” are not representative of random sequences, and testing positive for AIDS is not representative of not having AIDS (base rate).

21. Base Rates The “base rate” is the percentage of people in the population who have a certain property. The base rate of terrorists is the percentage of terrorists in the population, the base rate of HIV/AIDS cases is the percentage of people who have HIV/AIDS in the population, etc.

22. Base Rates As we have seen, base rates matter. If the base rate of a condition is very low (small percentage of terrorists), then even very accurate tests (100% true positive, 99% true negative) can be useless. .

23. Base Rate Neglect The “base rate neglect fallacy” is the fallacy of ignoring the base rate when making a judgment. For example, if I assumed you were a terrorist, because you tested positive, I would be committing the base rate neglect fallacy. I should assume you’re still probably not a terrorist, because the base rate of terrorists is so low.

24. Logic and fallacies

25. Arguments Philosophical argument consists of two parts: the premises and the conclusion. The premises are the ‘evidence’ that are given in support of the conclusion. The conclusion is the ‘claim’ that the premises are supposed to support.

26. Deductive Validity We say that an argument is deductively valid when it has the following property: If the premises of the argument are true, then the conclusion of the argument must be true. A valid argument is “truth-preserving”: the truth of the premises gets passed on to the conclusion.

27. Inductive Validity We say that an argument is inductively valid when it has the following property: If the premises are true, then the conclusion is likely to be true. An inductive argument probably preserves truth.

28. Invalidity An argument that is not valid is called invalid. Valid: If the premises are true, then the conclusion must be true. Invalid: The premises can be true while the conclusion is false.

29. Deductive Logic The goal of deductive logic is to identify deductively valid argument forms. We can use these as a formal test for validity: if an argument has a certain form, then that argument is deductively valid. There is no formal test for (deductive) invalidity: no way of looking at the form of an argument and telling that the premises do not guarantee the conclusion.

30. Fallacies A fallacy is an invalid argument, usually one that might mislead someone into thinking it’s valid.

31. Straw Man Fallacy The Straw Man Fallacy is when you misrepresent your opponent, and argue against the misrepresentation, rather than against your opponents claim.

32. Assuming the Original Conclusion Assuming the original conclusion involves trying to show that a claim is true by assuming that it is true in the premises. It has the form: X is true. Why? Because X.

33. False Equivocation Equivocation (or “false equivocation”) is when one word is used with two meanings in the same argument, rendering it invalid.

34. False Dilemma An argument commits the false dilemma fallacy when it presents two options as the only options, even though there are actually more options.

35. Fallacy of the Mean The fallacy of the mean is the assumption that a “middle point” between two views is the right one.

36. Distribution Fallacy The distribution fallacy is committed when one assumes that individuals have the properties of groups to which they belong. Lingnan has an excellent philosophy department. I am a philosopher at Lingnan._________ Therefore, I am an excellent philosopher.

37. Composition Fallacy The converse of the distribution fallacy is the composition fallacy, assuming that groups have the properties of the individuals that compose them. For example: “A point doesn’t have any length; lines are made out of points; therefore, a line doesn’t have any length.”

38. Ecological Fallacy Here’s an “ecological inference”. Countries where, on average, people consume more fat have higher rates of breast cancer. Therefore, there is a correlation between the amount of fat a person consumes and his/ her chances of breast cancer.

39. Argument from Ignorance The argument from ignorance goes like this: “You can’t prove that God doesn’t exist. Therefore God exists.” It assumes that because there is no argument against a position, that that position must be correct.

40. Genetic Fallacy The genetic fallacy seeks to evaluate a claim on the basis of its origin. So, for example, someone might say, “Eugenics is wrong, because the Nazis began it and did horrible things for its sake.” Eugenics may be wrong, but the fact that the Nazis began it is irrelevant to this claim.

41. Appeal to Motive Sometimes people argue that a certain claim must be false, or an argument invalid, because of the motives of the person making the claim/ argument.

42. Naturalistic Fallacy The Naturalistic Fallacyassumes that just because something is natural, it is good, and just because something is not natural it is not good. Some natural things are bad, some non-natural things are good.

43. The scientific method

44. Scientific Method Last time we discussed the hypothetico-deductive method, which consisted of four steps: • Formulate a hypothesis • Generate testable predictions • Gather data • Check predictions against observations

45. Good Theories A good theory should: • Have predictive power • Explain the relevant phenomenon in terms of underlying causal mechanisms • Be fruitful • Be simple • Be coherent

46. Causation Much of science is concerned with discovering the causal structure of the world. We want to understand what causes what so we can predict, explain, and control the events around us.

47. Correlation Two variables A, B that are not independent are said to be correlated. A and B are positively correlated when P(A/ B) > P(A). If B happens, A is more likely to happen. A and B are negatively correlated when P(A/ B) < P(A). If B happens, A is less likely to happen.

48. Correlation Other relationships between variables are often called correlation as well. A and B are positively correlated when increases in A correspond to increases in B. A and B are negatively correlated when increases in A correspond to decreases in B.

49. Causation and Correlation One thing that can lead two variables A and B to be correlated is when A causes B. For example, if having a cold causes a runny nose, then having a cold is correlated with having a runny nose: P(cold/ runny nose) > P(cold)

50. Causation ≠ Correlation But causation does not imply correlation. If A and B are correlated there are several possibilities: • A causes B • B causes A • C causes A and C causes B • A and B are only accidentally correlated