Trusting Science

1. Trusting Science

2. The Flat Earth Society • Believes—or claims to—that the Earth is really flat. • Why are they wrong? what’s the counterevidence? • most of us don’t know; haven’t collected the data; don’t know what the data even are, why that’s evidence • Yet,we rightly reject flat-earthism.

3. Parity of reasoning • If you think that something is good evidence in one context, you should think it’s good evidence in another context, unless you can find a relevant difference. • If you reject flat-earthism—and you don’t really know what the evidence against it is—then it’s because you trust the testimony of scientists. • But then, you should trust their testimony even when they make claims you don’t like.

4. Other reasons to trust science • Track record: • has long history of surprising predictions • Default position for testimony: • most speakers are sincere and aware of where their competence lies • Institutional structure: • science is competitive, collaborative venture; strong pressures (financial, reputational, etc.) to engage in cautious research

5. More on institutional structure • Prestige as primary motivation for scientists • respect of other scientists • Secondary motivations: • Money • but grant money for further research • Peer pressure • but not entirely a matter of conformity • petty vindictiveness, etc.

6. More on institutional structure • If my work is based on your false results, I’m wasting my time. • If I publish false results, I’m risking my reputation—more or less the only thing I have. • If I can show that your results are false, I’ll receive credit, attention. • The most famous scientists are the ones who overturned established theories.

7. More on institutional structure • Science and conspiracy theories: • Conspiracy theories always implausible • People aren’t good at keeping secrets, • especially in science case, because the motivational system isn’t conducive. • There’s never evidence for a good conspiracy; • good conspirators cover their tracks.

8. The Nature of Science

9. Scientific claims are proven? • No! • Proof = deduction from premises known with certainty. • Science is inductive. • claims about things we haven’t yet observed, by inductive generalization from past observations • claims about unobserved mechanisms, by inference to the best explanation

10. But the strongest kind of induction: • data collected and recollected • often using very sophisticated equipment • scrutinized and challenged by other scientists • over periods of decades, by thousands of the smartest people alive • with incentives to pursue defense of competing theories

11. (Naive) Falsificationism due to Karl Popper • Scientific hypotheses can never be conclusively verified, but they can be falsified. • ‘All swans are white’ can never be proven true, but can be proven false. • Aim of a theory should not be verification, but survival of attempted falsification. • A theory is scientific if and only if it is falsifiable; • an unfalsifiable theory is one that refuses to make predictions.

12. But falsificationism is false • Newtonian mechanics: three laws of motion plus law of gravitation. • Makes no predictions at all • Therefore, unfalsifiable

13. Add auxiliary hypotheses • e.g., Initial positions, movements, masses of various planets • Nowit makes predictions, is falsifiable. • But any theory can be rendered falsifiable with addition of auxiliary hypotheses.

14. Some (oft abused) terminology • Hypothesis: claim that has not been directly observed to be true. • Theory: system of hypotheses, designed to explain and predict data/observations. • Data/observations: facts that have been directly empirically verified.

15. Some (oft abused) terminology • Conjecture: hypothesis for which there is yet little/no evidence . • Fact: true claim/statement/proposition • some hypotheses are facts, some aren’t; some are well-supported, some arent. • likewise for theories • Can’t conclude that x is a conjecture, from the fact that x is a hypothesis • Can’t define theory as something well established

16. Established Science

17. Parity of reasoning argument from earlier only pertains to established science: science that has stood the test of time and is widely accepted in the scientific community. • Replication: getting the same findings by different people, in different conditions. • is evidence against fluke, fraud, unnoticed confound

18. Science reporting in non-science press • Usually preliminary findings, not yet replicated • Often presented with click-bait titles that indicate low standards • Sometimes simplified for non-science audience to the point of being very misleading

19. p-hacking • By measuring a large number of dependent and independent variables, • and stopping the study when a significant effect is found (p-value of < .05), • it’s easy to get spurious results. • One team “discovered” that listening to the Beatles song “When I’m Sixty-Four” made subjects 1.5 years younger! • Aclear example of why replication is important

20. Applying what we’ve learned Vaccines and Autism

21. Internet is rife with moving first-personal accounts from mothers of their children being vaccinated, then developing autism, • But the only scientific evidence of vaccine-autism link (Wakefield et al. study) has been retracted due to fraud allegations.

22. “I saw it with my own eyes.” • Hume: no one ever perceives any causal connection, only correlation. • Inferring causation from a single instance is post hoc ergo propter hoc fallacy.

23. “I saw it with my own eyes.” • Intuitive processor is wired to commit post hoc fallacy rather than risk missing genuine causal relation. • If you think you’ve perceived a causal connection, you’ve most likely experienced the post hoc illusion.

24. MMR/Autism link • How many children were vaccinated and have the condition? • How many children were vaccinated and did not develop it? • How many children were not vaccinated and have the condition? • How many children were not vaccinated and did not develop it? To establish even a correlation, we’d need to know:

25. Even a large number of case (i) doesn’t show a correlation, let alone causation, • because it doesn’t show that children receiving vaccine are more likely to develop autism than those who don’t. • “Plural of anecdote is not data”

26. Self-selection • Suppose we found a representative sample of all MMR/autism reports on the Internet, • would that give us reliable correlation data? • Only if parents of case (ii)–(iv) children were just as likely to post their experience as parents of case (i) children • it’s very unlikely that they would be.

27. Write in; tell us what you think! • Should the opinions of random Internet users influence our opinions on scientific matters? • Medical/scientific issues are not properly decidable by popular opinion. • ad populum fallacy: lots of people believe it; therefore it’s true!

28. Holding heterodox opinions about scientific matters • If you hold a scientific view outside of the mainstream, you’re committed to the following argument: • Most scientific experts believe p. • But (I believe) not-p. • Most scientific experts are wrong about the thing they spend their lives trying to get right.

29. Holding heterodox opinions about scientific matters • Either: • (a) I’ve made a lucky guess or • (b) I know more about the science than they do. • (b) seems implausibly arrogant • if (a), then I don’t have any reason to believe not-p • Personal experience is not evidence of causation

30. Not just science • If consensus among professional historians holds p, it’s usually irrational to deny p. • Holocaust denial • Contrast with religion, politics • there isn’t consensus. • there are many debates about values, not facts.

31. Science and intrinsically implausible testimony • If you testify to something intrinsically implausible, it’s reasonable for me to reject your claim, infer that you’re lying or mistaken. • Not so with science! • Why not? • Science’s track record of intrinsically implausible—but true—claims