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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 contrast 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 calculus 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 handrail 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 textbook 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 of 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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mainly because 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 of 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 a 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 to 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 gray,

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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 the 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.D.s,

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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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00:15:51,875 --> 00:15:54,481
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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00:16:04,249 --> 00:16:07,281
Well, here's the paradox of modern science,

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the paradox of the conclusion I think historians

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00:16:09,553 --> 00:16:12,154
and philosophers and sociologists have come to,

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that actually science is the appeal to authority,

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00:16:15,655 --> 00:16:19,431
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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00:16:25,695 --> 00:16:28,809
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,

395
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but a very special kind of crowd.

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00:16:35,921 --> 00:16:37,811
Science does appeal to authority,

397
00:16:37,811 --> 00:16:39,861
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,

401
00:16:46,840 --> 00:16:48,738
of all of the scientists who have worked

402
00:16:48,738 --> 00:16:51,455
on a particular problem.

403
00:16:51,455 --> 00:16:54,251
Scientists have a kind of culture of collective distrust,

404
00:16:54,251 --> 00:16:56,451
this "show me" culture,

405
00:16:56,451 --> 00:16:58,401
illustrated by this nice woman here

406
00:16:58,401 --> 00:17:01,483
showing her colleagues her evidence.

407
00:17:01,483 --> 00:17:03,340
Of course, these people don't
really look like scientists,

408
00:17:03,340 --> 00:17:05,326
because they're much too happy.

409
00:17:05,326 --> 00:17:09,338
(Laughter)

410
00:17:09,338 --> 00:17:13,660
Okay, so that brings me to my final point.

411
00:17:13,660 --> 00:17:16,308
Most of us get up in the morning.

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00:17:16,308 --> 00:17:17,718
Most of us trust our cars.

413
00:17:17,718 --> 00:17:19,260
Well, see, now I'm thinking, I'm in Manhattan,

414
00:17:19,260 --> 00:17:20,558
this is a bad analogy,

415
00:17:20,558 --> 00:17:23,382
but most Americans who don't live in Manhattan

416
00:17:23,382 --> 00:17:25,120
get up in the morning and get in their cars

417
00:17:25,120 --> 00:17:27,649
and turn on that ignition, and their cars work,

418
00:17:27,649 --> 00:17:29,650
and they work incredibly well.

419
00:17:29,650 --> 00:17:32,365
The modern automobile hardly ever breaks down.

420
00:17:32,365 --> 00:17:35,148
So why is that? Why do cars work so well?

421
00:17:35,148 --> 00:17:37,652
It's not because of the genius of Henry Ford

422
00:17:37,652 --> 00:17:40,743
or Karl Benz or even Elon Musk.

423
00:17:40,743 --> 00:17:42,885
It's because the modern automobile

424
00:17:42,885 --> 00:17:47,919
is the product of more than 100 years of work

425
00:17:47,919 --> 00:17:49,509
by hundreds and thousands

426
00:17:49,509 --> 00:17:50,845
and tens of thousands of people.

427
00:17:50,845 --> 00:17:52,956
The modern automobile is the product

428
00:17:52,956 --> 00:17:55,745
of the collected work and wisdom and experience

429
00:17:55,745 --> 00:17:58,092
of every man and woman who has ever worked

430
00:17:58,092 --> 00:17:59,700
on a car,

431
00:17:59,700 --> 00:18:02,615
and the reliability of the technology is the result

432
00:18:02,615 --> 00:18:05,298
of that accumulated effort.

433
00:18:05,298 --> 00:18:08,155
We benefit not just from the genius of Benz

434
00:18:08,155 --> 00:18:09,221
and Ford and Musk

435
00:18:09,221 --> 00:18:11,989
but from the collective intelligence and hard work

436
00:18:11,989 --> 00:18:14,240
of all of the people who have worked

437
00:18:14,240 --> 00:18:15,910
on the modern car.

438
00:18:15,910 --> 00:18:17,960
And the same is true of science,

439
00:18:17,960 --> 00:18:20,804
only science is even older.

440
00:18:20,804 --> 00:18:23,378
Our basis for trust in science is actually the same

441
00:18:23,378 --> 00:18:26,052
as our basis in trust in technology,

442
00:18:26,052 --> 00:18:30,039
and the same as our basis for trust in anything,

443
00:18:30,039 --> 00:18:32,317
namely, experience.

444
00:18:32,317 --> 00:18:34,161
But it shouldn't be blind trust

445
00:18:34,161 --> 00:18:36,921
any more than we would have blind trust in anything.

446
00:18:36,921 --> 00:18:39,762
Our trust in science, like science itself,

447
00:18:39,762 --> 00:18:41,675
should be based on evidence,

448
00:18:41,675 --> 00:18:43,177
and that means that scientists

449
00:18:43,177 --> 00:18:45,225
have to become better communicators.

450
00:18:45,225 --> 00:18:48,112
They have to explain to us not just what they know

451
00:18:48,112 --> 00:18:49,840
but how they know it,

452
00:18:49,840 --> 00:18:53,730
and it means that we have
to become better listeners.

453
00:18:53,730 --> 00:18:55,149
Thank you very much.

454
00:18:55,149 --> 00:18:57,452
(Applause)