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Every day we face issues like climate change
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or the safety of vaccines
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where we have to answer questions whose answers
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rely heavily on scientific information.
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Scientists tell us that the world is warming.
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Scientists tell us that vaccines are safe.
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But how do we know if they are right?
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Why should be believe the science?
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The fact is, many of us actually
don't believe the science.
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Public opinion polls consistently show
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that significant proportions of the American people
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don't believe the climate is
warming due to human activities,
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don't think that there is
evolution by natural selection,
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and aren't persuaded by the safety of vaccines.
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So why should we believe the science?
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Well, scientists don't like talking about
science as a matter of belief.
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In fact, they would contract science with faith,
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and they would say belief is the domain of faith.
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And faith is a separate thing
apart and distinct from science.
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Indeed they would say religion is based on faith
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or maybe the calculous of Pascal's wager.
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Blaise Pascal was a 17th century mathematician
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who tried to bring scientific
reasoning to the question of
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whether or not he should believe in God,
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and his wager went like this:
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well, if God doesn't exist
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but I decide to believe in him
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nothing much is really lost.
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Maybe a few hours on Sunday.
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[Laughter]
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But if he does exist and I don't believe in him,
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then I'm in deep trouble.
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And so Pascal said, we'd better believe in God.
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Or as one of my college professors said,
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"He clutched for the handmill of faith."
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He made that leap of faith
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leaving science and rationalism behind.
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Now the fact is though, for most of us,
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most scientific claims are a leap of faith.
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We can't really judge scientific
claims for ourselves in most cases.
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And indeed this is actually
true for most scientists as well
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outside of their own specialties.
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So if you think about it, a geologist can't tell you
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whether a vaccine is safe.
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Most chemists are not experts in evolutionary theory.
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A physicist cannot tell you,
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despite the claims of some of them,
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whether or not tobacco causes cancer.
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So, if even scientists themselves
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have to make a leap of faith
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outside their own fields,
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then why do they accept the
claims of other scientists?
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Why do they believe each other's claims?
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And should we believe those claims?
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So what I'd like to argue is yes, we should,
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but not for the reason that most of us think.
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Most of us were taught in school
that the reason we should
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believe in science is because of the scientific method.
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We were taught that scientists follow a method
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and that this method guarantees
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the truth of their claims.
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The method that most of us were taught in school,
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we can call it the text book method,
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is the hypothetical deductive method.
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According to the standard
model, the textbook model,
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scientists develop hypotheses, they deduce
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the consequences for those hypotheses,
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and then they go out into the world and they say,
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"Okay, well are those consequences true?"
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Can we observe them taking
place in the natural world?
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And if they are true, then the scientists say,
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"Great, we know the hypothesis is correct."
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So there are many famous examples in the history
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of science of scientists doing exactly this.
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One of the most famous examples
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comes from the work of Albert Einstein.
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When Einstein developed the
theory of general relativity,
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one of the consequences of his theory
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was that space time wasn't just an empty void
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but that it actually had a fabric.
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And that that fabric was bent
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in the presence of massive objects like the sun.
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So if this theory were true then it meant that light
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as it passed the sun
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should actually be bent around it.
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That was a pretty startling prediction
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and it took a few years before scientists
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were able to test it
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but they did test it in 1919,
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and lo and behold it turned out to be true.
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Starlight actually does bend
as it travels around the sun.
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This was a huge confirmation of the theory.
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It was considered proof of the truth
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of this radical new idea,
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and it was written up in many newspapers
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around the globe.
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Now sometimes this theory or this model
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is referred to as the deductive-nomological model.
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Meaning those academics
like to make things complicated.
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But also because in the ideal case, it's about laws.
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So nomological means having to do with laws.
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And in the ideal case, the hypothesis isn't just an idea:
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ideally, it is a law of nature.
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Why does it matter that it is a law of nature?
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Because if it is a law, it can't be broken.
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If it's a law then it will always be true
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in all times and all places
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no matter what the circumstances are.
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And all of you know at least
one example of a famous law:
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Einstein's famous equation, E=MC2,
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which tells us what the relationship is
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between energy and mass.
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And that relationship is true no matter what.
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Now, it turns out though that there are
several problems with this model.
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The main problem is that it's wrong.
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It's just not true. (Laughter)
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And I'm going to talk about
three reasons why it's wrong.
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So the first reason is a logical reason.
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It's the problem of the fallacy
of affirming the consequent.
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So that's another fancy academic way of saying
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that false theories can make true predictions.
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So just because the prediction comes true
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doesn't actually logically
prove that the theory is correct.
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And I have a good example of that too,
again from the history of science.
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This is a picture of the Ptolemaic universe
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with the Earth at the center of the universe
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and the sun and the planets going around it.
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The Ptolemaic model was believed
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by many very smart people for many centuries.
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Well, why?
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Well the answer is because it made
lots of predictions that came true.
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The Ptolemaic system enabled astronomers
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to make accurate predictions
of the motions of the planet,
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in fact more accurate predictions at first
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than the Copernican theory
which we now would say is true.
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So that's one problem with the textbook model.
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A second problem is a practical problem,
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and it's the problem of auxiliary hypotheses.
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Auxiliary hypotheses are assumptions
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that scientists are making
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that they may or may not even
be aware that they're making.
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So an important example of this
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comes from the Copernican model,
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which ultimately replaced the Ptolemaic system.
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So when Nicolaus Copernicus said,
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actually the Earth is not the center of the universe,
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the sun is the center of the solar system,
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the Earth moves around the sun.
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Scientists said, well okay, Nicolaus, if that's true
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we ought to be able to detect the motion
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of the Earth around the sun.
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And so this slide here illustrates a concept
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known as stellar parallax.
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And astronomers said, if the Earth is moving
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and we look at a prominent star, let's say, Sirius
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—well I know I'm in Manhattan
so you guys can't see the stars,
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but imagine you're out in the country,
imagine you chose that rural life—
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and we look at a star in December, we see that star
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against the backdrop of distant stars.
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If we now make the same observation six months later
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when the Earth has moved to this position in June,
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we look at that same star and we see it against a different backdrop.
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That difference, that angular
difference, is the stellar parallax.
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So this is the prediction that
the Copernican model makes.
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Astronomers looked for the stellar parallax
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and they found nothing, nothing at all.
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And many people argued that this proved
that the Copernican model was false.
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So what happened?
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Well in hindsight we can say that astronomers were making
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two auxiliary hypotheses, both of which
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we would now say were incorrect.
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The first was an assumption about the size of the Earth's orbit.
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Astronomers were assuming that the Earth's orbit was large
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relative to the distance of the stars.
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Today we would draw the picture more like this,
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this comes from NASA,
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and you see the Earth's orbit is actually quite small.
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In fact, it's actually much
smaller even than shown here.
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The stellar parallax therefore,
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is very small and actually very hard to detect.
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And that leads to the second reason
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why the prediction didn't work,
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because scientists were also assuming
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that the telescopes they had were sensitive enough
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to detect the parallax.
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And that turned out not to be true.
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It wasn't until the 19th century
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that scientists were able to detect
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the stellar parallax.
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So, there's a third problem as well.
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The third problem is simply a factual problem,
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that a lot of science doesn't fit the textbook model.
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A lot of science isn't deductive at all,
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it's actually inductive.
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And by that we mean that scientists don't necessarily
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start with theories and hypotheses,
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often they just start with observations
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of stuff going on in the world.
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And the most famous example
of that is one of the most
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famous scientists who ever lived, Charles Darwin.
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When Darwin went out as a young
man on the voyage of the Beagle,
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he didn't have a hypothesis, he didn't have a theory.
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He just knew that he wanted
to have a career as a scientist
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and he started to collect data.
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Mainly he knew that he hated medicine
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because the sight of blood made him sick so
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he had to have an alternative career path.
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So he started collecting data.
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And he collected many things including his famous finches.
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When he collected these finches,
he threw them in a bag
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and he had no idea what they meant.
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Many years later back in London,
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Darwin looked at his data again and began
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to develop an explanation,
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and that explanation was the
theory of natural selection.
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Besides inductive science,
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scientists also often participate in modeling.
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One of the things scientists want to do in life
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is to explain the causes of things.
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And how do we do that?
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Well, one way you can do it is to build a model
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that tests an idea.
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So this is a picture of Henry Cadell,
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who was a Scottish geologist in the 19th century.
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You can tell he's Scottish because he's wearing
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a deerstalker cap and Wellington boots.
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(Laughter)
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And Cadell wanted to answer the question,
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how are mountains formed?
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And one of the things he had observed
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is that if you look at mountains
like the Appalachians,
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you often find that the rocks in them
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are folded,
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and they're folded in a particular way,
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which suggested to him
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that they were actually being
compressed from the side.
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And this idea would later play a major role
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in discussions of continental drift.
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So he built this model, this crazy contraption
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with levers and wood and here's his wheelbarrow,
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buckets, a big sledgehammer.
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I don't know why he's got the Wellington boots.
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Maybe it's going to rain.
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And he created this physical model in order
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to demonstrate that you could in fact create
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patterns in rocks, or at least in this case in mud,
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that looked a lot like mountains
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if you compressed them from the side.
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So it was an argument about
the cause of mountains.
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Nowadays, most scientists prefer to work inside,
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so they don't build physical models so much
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as to make computer simulations.
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But a computer simulation is a kind of a model.
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It's a model that's made with mathematics,
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and like the physical models of the 19th century,
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it's very important for thinking about causes.
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So one of the big questions
to do with climate change,
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we have tremendous amounts of evidence
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that the earth is warming up.
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This slide here, the black line shows
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the measurements that scientists have taken
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for the last 150 years
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showing that the earth's temperature
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has steadily increased,
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and you can see in particular
that in the last 50 years
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there's been this dramatic increase
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of nearly one degree Centigrade,
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or almost two degrees Fahrenheit.
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So what, though, is driving that change?
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How can we know what's causing
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the observed warming?
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Well, scientists can model it
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using a computer simulation.
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So this diagram illustrates a computer simulation
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that has looked at all the different factors
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that we know can influence the earth's climate,
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so sulfate particles from air pollution,
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volcanic dust from volcanic eruptions,
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changes in solar radiation,
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and, of course, greenhouse gases.
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And they asked the question,
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what set of variables put into a model
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will reproduce what we actually see in real life?
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So here is the real life in black.
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Here's the model in this light grey,
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and the answer is
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a model that includes, it's the answer E on that SAT,
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all of the above.
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The only way you can reproduce
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the observed temperature measurements
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is with all of these things put together,
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including greenhouse gases,
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and in particular you can see that the increase
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in greenhouse gases tracks
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this very dramatic increase in temperature
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over the last 50 years.
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And so this is why climate scientists say
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it's not just that we know that
climate change is happening,
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we know that greenhouse gases are a major part
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of the reason why.
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So now because there all these different things
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that scientists do,
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the philosopher Paul Feyerabend famously said,
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"The only principle in science
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that doesn't inhibit progress is: anything goes."
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Now this quotation has often
been taken out of context,
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because Feyerabend was not actually saying
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that in science anything goes.
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What he was saying was,
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actually the full quotation is,
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"If you press me to say
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what is the method of science,
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I would have to say: anything goes."
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What he was trying to say
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is that scientists do a lot of different things.
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Scientists are creative.
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But then this pushes the question back:
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if scientists don't use a single method,
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then how do they decide
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what's right and what's wrong?
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And who judges?
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And the answer is, scientists judge,
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and they judge by judging evidence.
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Scientists collect evidence in many different ways,
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but however they collect it,
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they have to subject it to scrutiny.
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And this led to sociologist Robert Merton
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to focus on this question of how scientists
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scrutinize data and evidence,
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and he said they do it in a way he called
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"organized skepticism."
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And by that he meant it's organized
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because they do it collectively,
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they do it as a group,
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and skepticism, because they do it from a position
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of distrust.
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That is to say, the burden of proof
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is on the person with a novel claim.
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And in this sense, science
is intrinsically conservative.
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It's quite hard to persuade the scientific community
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to say, "Yes, we know something, this is true."
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So despite the popularity of the concept
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of paradigm shifts,
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what we find is that actually,
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really major changes in scientific thinking
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are relatively rare in the history of science.
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So finally that brings us to one more idea:
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if scientists judge evidence collectively,
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this has led historians to focus on the question
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of consensus,
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and to say that at the end of the day,
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what science is,
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what scientific knowledge is,
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is the consensus of the scientific experts
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who through this process of organized scrutiny,
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collective scrutiny,
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have judged the evidence
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and come to a conclusion about it,
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either yea or nay.
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So we can think of scientific knowledge
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as a consensus of experts.
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We can also think of science as being
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a kind of a jury,
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except it's a very special kind of jury.
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It's not a jury of your peers,
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it's a jury of geeks.
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It's a jury of men and women with Ph.Ds,
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and unlike a conventional jury,
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which has only two choices,
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guilty or not guilty,
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the scientific jury actually has a number of choices.
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Scientists can say yes, something's true.
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Scientists can say no, it's false.
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Or, they can say, well it might be true
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but we need to work more
and collect more evidence.
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Or, they can say it might be true,
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but we don't know how to answer the question
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and we're going to put it aside
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and maybe we'll come back to it later.
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That's what scientists call "intractable."
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But this leads us to one final problem:
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if science is what scientists say it is,
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then isn't that just an appeal to authority?
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And weren't we all taught in school
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that the appeal to authority is a logical fallacy?
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Well, here's the paradox of modern science,
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the paradox of the conclusions I think historians
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and philosophers and sociologists have come to,
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that actually science is the appeal to authority,
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but it's not the authority of the individual,
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no matter how smart that individual is,
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like Plato or Socrates or Einstein.
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It's the authority of the collective community.
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You can think of it is a kind of wisdom of the crowd,
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but a very special kind of crowd.
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Science does appeal to authority,
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but it's not based on any individual,
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no matter how smart that individual may be.
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It's based on the collective wisdom,
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the collective knowledge, the collective work,
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of all of the scientists who have worked
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on a particular problem.
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Scientists have a kind of culture of collective distrust,
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this "show me" culture,
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illustrated by this nice woman here
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showing her colleagues her evidence.
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Of course, these people don't
really look like scientists,
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because they're much too happy.
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(Laughter)
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Okay, so that brings me to my final point.
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Most of us get up in the morning.
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Most of us trust our cars.
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Well, see, now I'm thinking, I'm in Manhattan,
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this is a bad analogy,
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but most Americans who don't live in Manhattan
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get up in the morning and get in their cars
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and turn on that ignition, and their cars work,
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and they work incredibly well.
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The modern automobile hardly ever breaks down.
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So why is that? Why do cars work so well?
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It's not because of the genius of Henry Ford
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or Carl Benz or even Elon Musk.
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It's because the modern automobile
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is the product of more than 100 years of work
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by hundreds and thousands
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and tens of thousands of people.
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The modern automobile is the product
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of the collected work and wisdom and experience
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of every man and woman who has ever worked
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on a car,
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and the reliability of the technology is the result
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of that accumulated effort.
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We benefit not just from the genius of Benz
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and Ford and Musk
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but from the collective intelligence and hard work
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of all of the people who have worked
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on the modern car.
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And the same is true of science,
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only science is even older.
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Our basis for trust in science is actually the same
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as our basis in trust in technology,
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and the same as our basis for trust in anything,
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namely, experience.
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But it shouldn't be blind trust
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any more than we would have blind trust in anything.
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Our trust in science, like science itself,
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should be based on evidence,
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and that means that scientists
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have to become better communicators.
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They have to explain to us not just what they know
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but how they know it,
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and it means that we have
to become better listeners.
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Thank you very much.
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(Applause)
Adrian Dobroiu
2:50 is the hypothetical deductive method. --> the hypothetico-deductive method.