TEDSalon NY2014
Naomi Oreskes: Why we should trust scientists
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Many of the world's biggest problems require asking questions of scientists -- but why should we believe what they say? Historian of science Naomi Oreskes thinks deeply about our relationship to belief and draws out three problems with common attitudes toward scientific inquiry -- and gives her own reasoning for why we ought to trust science.
Naomi Oreskes - Historian of science
Naomi Oreskes is a historian of science who uses reason to fight climate change denial. Full bio
Naomi Oreskes is a historian of science who uses reason to fight climate change denial. Full bio
Double-click the English transcript below to play the video.
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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.
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,
warming due to human activities,
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don't think that there is
evolution by natural selection,
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.
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.
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
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.
claims for ourselves in most cases.
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And indeed this is actually
true for most scientists as well
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?
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
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,
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?
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,
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.
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.
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:
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.
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.
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.
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.
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.
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.
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,
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.
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.
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,
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 —
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.
see it against a different backdrop.
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That difference, that angular
difference, is the stellar parallax.
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.
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
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.
about the size of the Earth's orbit.
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Astronomers were assuming
that the Earth's orbit was large
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.
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
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,
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
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.
including his famous finches.
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When he collected these finches,
he threw them in a bag
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.
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,
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.
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.
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,
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
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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11:51
So this diagram illustrates a computer simulation
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11:54
that has looked at all the different factors
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11:56
that we know can influence the Earth's climate,
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11:59
so sulfate particles from air pollution,
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12:01
volcanic dust from volcanic eruptions,
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12:04
changes in solar radiation,
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12:07
and, of course, greenhouse gases.
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12:09
And they asked the question,
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12:11
what set of variables put into a model
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12:14
will reproduce what we actually see in real life?
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12:17
So here is the real life in black.
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12:19
Here's the model in this light gray,
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12:22
and the answer is
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12:23
a model that includes, it's the answer E on that SAT,
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4387
12:28
all of the above.
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12:30
The only way you can reproduce
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1506
12:31
the observed temperature measurements
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1828
12:33
is with all of these things put together,
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1978
12:35
including greenhouse gases,
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12:37
and in particular you can see that the increase
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12:40
in greenhouse gases tracks
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1884
12:42
this very dramatic increase in temperature
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12:44
over the last 50 years.
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12:45
And so this is why climate scientists say
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it's not just that we know that
climate change is happening,
climate change is happening,
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12:51
we know that greenhouse gases are a major part
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12:54
of the reason why.
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12:56
So now because there all these different things
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12:59
that scientists do,
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13:00
the philosopher Paul Feyerabend famously said,
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13:04
"The only principle in science
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13:05
that doesn't inhibit progress is: anything goes."
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13:09
Now this quotation has often
been taken out of context,
been taken out of context,
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2616
13:12
because Feyerabend was not actually saying
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13:14
that in science anything goes.
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13:16
What he was saying was,
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13:17
actually the full quotation is,
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13:19
"If you press me to say
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13:21
what is the method of science,
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13:23
I would have to say: anything goes."
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13:27
What he was trying to say
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13:28
is that scientists do a lot of different things.
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13:30
Scientists are creative.
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13:33
But then this pushes the question back:
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13:35
If scientists don't use a single method,
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13:38
then how do they decide
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13:40
what's right and what's wrong?
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13:42
And who judges?
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13:44
And the answer is, scientists judge,
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13:46
and they judge by judging evidence.
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13:49
Scientists collect evidence in many different ways,
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13:52
but however they collect it,
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13:54
they have to subject it to scrutiny.
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13:56
And this led the sociologist Robert Merton
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13:59
to focus on this question of how scientists
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14:01
scrutinize data and evidence,
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1679
14:03
and he said they do it in a way he called
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14:05
"organized skepticism."
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14:07
And by that he meant it's organized
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14:09
because they do it collectively,
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14:11
they do it as a group,
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1629
14:12
and skepticism, because they do it from a position
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14:15
of distrust.
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1454
14:17
That is to say, the burden of proof
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1962
14:18
is on the person with a novel claim.
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2481
14:21
And in this sense, science
is intrinsically conservative.
is intrinsically conservative.
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3143
14:24
It's quite hard to persuade the scientific community
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2572
14:27
to say, "Yes, we know something, this is true."
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14:30
So despite the popularity of the concept
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14:33
of paradigm shifts,
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14:34
what we find is that actually,
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14:36
really major changes in scientific thinking
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14:39
are relatively rare in the history of science.
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14:42
So finally that brings us to one more idea:
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14:46
If scientists judge evidence collectively,
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14:50
this has led historians to focus on the question
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14:52
of consensus,
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14:54
and to say that at the end of the day,
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14:55
what science is,
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1934
14:57
what scientific knowledge is,
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1670
14:59
is the consensus of the scientific experts
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3379
15:02
who through this process of organized scrutiny,
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2154
15:05
collective scrutiny,
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2305
15:07
have judged the evidence
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1242
15:08
and come to a conclusion about it,
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2797
15:11
either yea or nay.
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2477
15:13
So we can think of scientific knowledge
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1724
15:15
as a consensus of experts.
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2052
15:17
We can also think of science as being
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1772
15:19
a kind of a jury,
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1578
15:21
except it's a very special kind of jury.
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909010
2514
15:23
It's not a jury of your peers,
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2104
15:25
it's a jury of geeks.
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1896
15:27
It's a jury of men and women with Ph.D.s,
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3634
15:31
and unlike a conventional jury,
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2442
15:33
which has only two choices,
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1690
15:35
guilty or not guilty,
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2685
15:37
the scientific jury actually has a number of choices.
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3401
15:41
Scientists can say yes, something's true.
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2784
15:44
Scientists can say no, it's false.
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2580
15:46
Or, they can say, well it might be true
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2540
15:49
but we need to work more
and collect more evidence.
and collect more evidence.
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3044
15:52
Or, they can say it might be true,
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1616
15:53
but we don't know how to answer the question
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1700
15:55
and we're going to put it aside
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1310
15:56
and maybe we'll come back to it later.
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2923
15:59
That's what scientists call "intractable."
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4002
16:03
But this leads us to one final problem:
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2606
16:06
If science is what scientists say it is,
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2938
16:09
then isn't that just an appeal to authority?
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2541
16:11
And weren't we all taught in school
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1062
16:13
that the appeal to authority is a logical fallacy?
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3227
16:16
Well, here's the paradox of modern science,
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3032
16:19
the paradox of the conclusion I think historians
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967281
2272
16:21
and philosophers and sociologists have come to,
387
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2601
16:24
that actually science is the appeal to authority,
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3501
16:27
but it's not the authority of the individual,
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3776
16:31
no matter how smart that individual is,
390
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2399
16:33
like Plato or Socrates or Einstein.
391
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3865
16:37
It's the authority of the collective community.
392
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3114
16:40
You can think of it is a kind of wisdom of the crowd,
393
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2986
16:43
but a very special kind of crowd.
394
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4126
16:47
Science does appeal to authority,
395
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1890
16:49
but it's not based on any individual,
396
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2050
16:51
no matter how smart that individual may be.
397
999861
2586
16:54
It's based on the collective wisdom,
398
1002447
1751
16:56
the collective knowledge, the collective work,
399
1004198
2642
16:58
of all of the scientists who have worked
400
1006840
1898
17:00
on a particular problem.
401
1008738
2717
17:03
Scientists have a kind of culture of collective distrust,
402
1011455
2796
17:06
this "show me" culture,
403
1014251
2200
17:08
illustrated by this nice woman here
404
1016451
1950
17:10
showing her colleagues her evidence.
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3082
17:13
Of course, these people don't
really look like scientists,
really look like scientists,
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1857
17:15
because they're much too happy.
407
1023340
1986
17:17
(Laughter)
408
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4012
17:21
Okay, so that brings me to my final point.
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4322
17:25
Most of us get up in the morning.
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2648
17:28
Most of us trust our cars.
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1036308
1410
17:29
Well, see, now I'm thinking, I'm in Manhattan,
412
1037718
1542
17:31
this is a bad analogy,
413
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1298
17:32
but most Americans who don't live in Manhattan
414
1040558
2824
17:35
get up in the morning and get in their cars
415
1043382
1738
17:37
and turn on that ignition, and their cars work,
416
1045120
2529
17:39
and they work incredibly well.
417
1047649
2001
17:41
The modern automobile hardly ever breaks down.
418
1049650
2715
17:44
So why is that? Why do cars work so well?
419
1052365
2783
17:47
It's not because of the genius of Henry Ford
420
1055148
2504
17:49
or Karl Benz or even Elon Musk.
421
1057652
3091
17:52
It's because the modern automobile
422
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2142
17:54
is the product of more than 100 years of work
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5034
17:59
by hundreds and thousands
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1067919
1590
18:01
and tens of thousands of people.
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1336
18:02
The modern automobile is the product
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1070845
2111
18:04
of the collected work and wisdom and experience
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2789
18:07
of every man and woman who has ever worked
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1075745
2347
18:10
on a car,
429
1078092
1608
18:11
and the reliability of the technology is the result
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2915
18:14
of that accumulated effort.
431
1082615
2683
18:17
We benefit not just from the genius of Benz
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2857
18:20
and Ford and Musk
433
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1066
18:21
but from the collective intelligence and hard work
434
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2768
18:23
of all of the people who have worked
435
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2251
18:26
on the modern car.
436
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1670
18:27
And the same is true of science,
437
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2050
18:29
only science is even older.
438
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2844
18:32
Our basis for trust in science is actually the same
439
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2574
18:35
as our basis in trust in technology,
440
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2674
18:38
and the same as our basis for trust in anything,
441
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3987
18:42
namely, experience.
442
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2278
18:44
But it shouldn't be blind trust
443
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1844
18:46
any more than we would have blind trust in anything.
444
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2760
18:48
Our trust in science, like science itself,
445
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2841
18:51
should be based on evidence,
446
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1913
18:53
and that means that scientists
447
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1502
18:55
have to become better communicators.
448
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2048
18:57
They have to explain to us not just what they know
449
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2887
19:00
but how they know it,
450
1128112
1728
19:01
and it means that we have
to become better listeners.
to become better listeners.
451
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3890
19:05
Thank you very much.
452
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1419
19:07
(Applause)
453
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2303
ABOUT THE SPEAKER
Naomi Oreskes - Historian of scienceNaomi Oreskes is a historian of science who uses reason to fight climate change denial.
Why you should listen
Noami Oreskes is a professor of the History of Science and an affiliated professor of Earth and Planetary Sciences at Harvard University. She received her PhD at Stanford in 1990 in the Graduate Special Program in Geological Research and History of Science.
In her 2004 paper published in Science, "Beyond the Ivory Tower: The Scientific Consensus on Climate Change,” Oreskes analyzed nearly 1,000 scientific journals to directly assess the magnitude of scientific consensus around anthropogenic climate change. The paper was famously cited by Al Gore in his film An Inconvenient Truth and led Oreskes to testify in front of the U.S. Senate Committee on Environment and Public Works.
Oreskes is the co-author of the 2010 book Merchants of Doubt, which looks at how the tobacco industry attempted to cast doubt on the link between smoking and lung cancer, and the 2014 book The Collapse of Western Civilization: A View from the Future, which looks back at the present from the year 2093. Both are written with Erik M. Conway.
Naomi Oreskes | Speaker | TED.com