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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.

- Historian of science
Naomi Oreskes is a historian of science who uses reason to fight climate change denial. Full bio

Every day we face issues like climate change
00:12
or the safety of vaccines
00:16
where we have to answer questions whose answers
00:17
rely heavily on scientific information.
00:20
Scientists tell us that the world is warming.
00:23
Scientists tell us that vaccines are safe.
00:26
But how do we know if they are right?
00:29
Why should be believe the science?
00:31
The fact is, many of us actually
don't believe the science.
00:33
Public opinion polls consistently show
00:36
that significant proportions of the American people
00:39
don't believe the climate is
warming due to human activities,
00:42
don't think that there is
evolution by natural selection,
00:45
and aren't persuaded by the safety of vaccines.
00:48
So why should we believe the science?
00:52
Well, scientists don't like talking about
science as a matter of belief.
00:56
In fact, they would contrast science with faith,
00:59
and they would say belief is the domain of faith.
01:02
And faith is a separate thing
apart and distinct from science.
01:05
Indeed they would say religion is based on faith
01:09
or maybe the calculus of Pascal's wager.
01:12
Blaise Pascal was a 17th-century mathematician
01:15
who tried to bring scientific
reasoning to the question of
01:18
whether or not he should believe in God,
01:21
and his wager went like this:
01:23
Well, if God doesn't exist
01:25
but I decide to believe in him
01:28
nothing much is really lost.
01:30
Maybe a few hours on Sunday.
01:32
(Laughter)
01:34
But if he does exist and I don't believe in him,
01:35
then I'm in deep trouble.
01:38
And so Pascal said, we'd better believe in God.
01:40
Or as one of my college professors said,
01:43
"He clutched for the handrail of faith."
01:45
He made that leap of faith
01:47
leaving science and rationalism behind.
01:49
Now the fact is though, for most of us,
01:54
most scientific claims are a leap of faith.
01:56
We can't really judge scientific
claims for ourselves in most cases.
02:00
And indeed this is actually
true for most scientists as well
02:04
outside of their own specialties.
02:07
So if you think about it, a geologist can't tell you
02:09
whether a vaccine is safe.
02:12
Most chemists are not experts in evolutionary theory.
02:13
A physicist cannot tell you,
02:16
despite the claims of some of them,
02:19
whether or not tobacco causes cancer.
02:20
So, if even scientists themselves
02:24
have to make a leap of faith
02:26
outside their own fields,
02:27
then why do they accept the
claims of other scientists?
02:29
Why do they believe each other's claims?
02:33
And should we believe those claims?
02:35
So what I'd like to argue is yes, we should,
02:39
but not for the reason that most of us think.
02:41
Most of us were taught in school
that the reason we should
02:44
believe in science is because of the scientific method.
02:47
We were taught that scientists follow a method
02:50
and that this method guarantees
02:53
the truth of their claims.
02:55
The method that most of us were taught in school,
02:57
we can call it the textbook method,
03:01
is the hypothetical deductive method.
03:02
According to the standard
model, the textbook model,
03:05
scientists develop hypotheses, they deduce
03:08
the consequences of those hypotheses,
03:11
and then they go out into the world and they say,
03:14
"Okay, well are those consequences true?"
03:15
Can we observe them taking
place in the natural world?
03:18
And if they are true, then the scientists say,
03:21
"Great, we know the hypothesis is correct."
03:24
So there are many famous examples in the history
03:27
of science of scientists doing exactly this.
03:29
One of the most famous examples
03:32
comes from the work of Albert Einstein.
03:34
When Einstein developed the
theory of general relativity,
03:36
one of the consequences of his theory
03:38
was that space-time wasn't just an empty void
03:41
but that it actually had a fabric.
03:44
And that that fabric was bent
03:45
in the presence of massive objects like the sun.
03:47
So if this theory were true then it meant that light
03:50
as it passed the sun
03:53
should actually be bent around it.
03:55
That was a pretty startling prediction
03:57
and it took a few years before scientists
03:59
were able to test it
04:01
but they did test it in 1919,
04:03
and lo and behold it turned out to be true.
04:05
Starlight actually does bend
as it travels around the sun.
04:07
This was a huge confirmation of the theory.
04:11
It was considered proof of the truth
04:13
of this radical new idea,
04:15
and it was written up in many newspapers
04:16
around the globe.
04:18
Now, sometimes this theory or this model
04:21
is referred to as the deductive-nomological model,
04:23
mainly because academics like
to make things complicated.
04:26
But also because in the ideal case, it's about laws.
04:30
So nomological means having to do with laws.
04:35
And in the ideal case, the hypothesis isn't just an idea:
04:38
ideally, it is a law of nature.
04:41
Why does it matter that it is a law of nature?
04:43
Because if it is a law, it can't be broken.
04:46
If it's a law then it will always be true
04:48
in all times and all places
04:50
no matter what the circumstances are.
04:52
And all of you know of at least
one example of a famous law:
04:54
Einstein's famous equation, E=MC2,
04:57
which tells us what the relationship is
05:01
between energy and mass.
05:03
And that relationship is true no matter what.
05:05
Now, it turns out, though, that there
are several problems with this model.
05:09
The main problem is that it's wrong.
05:13
It's just not true. (Laughter)
05:16
And I'm going to talk about
three reasons why it's wrong.
05:20
So the first reason is a logical reason.
05:22
It's the problem of the fallacy
of affirming the consequent.
05:25
So that's another fancy, academic way of saying
05:29
that false theories can make true predictions.
05:31
So just because the prediction comes true
05:34
doesn't actually logically
prove that the theory is correct.
05:36
And I have a good example of that too,
again from the history of science.
05:39
This is a picture of the Ptolemaic universe
05:43
with the Earth at the center of the universe
05:46
and the sun and the planets going around it.
05:48
The Ptolemaic model was believed
05:50
by many very smart people for many centuries.
05:52
Well, why?
05:56
Well the answer is because it made
lots of predictions that came true.
05:57
The Ptolemaic system enabled astronomers
06:01
to make accurate predictions
of the motions of the planet,
06:03
in fact more accurate predictions at first
06:06
than the Copernican theory
which we now would say is true.
06:08
So that's one problem with the textbook model.
06:12
A second problem is a practical problem,
06:15
and it's the problem of auxiliary hypotheses.
06:18
Auxiliary hypotheses are assumptions
06:21
that scientists are making
06:24
that they may or may not even
be aware that they're making.
06:26
So an important example of this
06:29
comes from the Copernican model,
06:31
which ultimately replaced the Ptolemaic system.
06:33
So when Nicolaus Copernicus said,
06:37
actually the Earth is not the center of the universe,
06:39
the sun is the center of the solar system,
06:41
the Earth moves around the sun.
06:43
Scientists said, well okay, Nicolaus, if that's true
06:45
we ought to be able to detect the motion
06:48
of the Earth around the sun.
06:50
And so this slide here illustrates a concept
06:52
known as stellar parallax.
06:54
And astronomers said, if the Earth is moving
06:56
and we look at a prominent star, let's say, Sirius --
07:00
well I know I'm in Manhattan
so you guys can't see the stars,
07:03
but imagine you're out in the country,
imagine you chose that rural life —
07:05
and we look at a star in December, we see that star
07:09
against the backdrop of distant stars.
07:12
If we now make the same observation six months later
07:15
when the Earth has moved to this position in June,
07:18
we look at that same star and we
see it against a different backdrop.
07:22
That difference, that angular
difference, is the stellar parallax.
07:26
So this is a prediction that the Copernican model makes.
07:30
Astronomers looked for the stellar parallax
07:33
and they found nothing, nothing at all.
07:35
And many people argued that this proved
that the Copernican model was false.
07:40
So what happened?
07:44
Well, in hindsight we can say
that astronomers were making
07:46
two auxiliary hypotheses, both of which
07:48
we would now say were incorrect.
07:51
The first was an assumption
about the size of the Earth's orbit.
07:53
Astronomers were assuming
that the Earth's orbit was large
07:57
relative to the distance to the stars.
08:00
Today we would draw the picture more like this,
08:02
this comes from NASA,
08:05
and you see the Earth's orbit is actually quite small.
08:06
In fact, it's actually much
smaller even than shown here.
08:09
The stellar parallax therefore,
08:12
is very small and actually very hard to detect.
08:13
And that leads to the second reason
08:17
why the prediction didn't work,
08:19
because scientists were also assuming
08:21
that the telescopes they had were sensitive enough
08:23
to detect the parallax.
08:26
And that turned out not to be true.
08:27
It wasn't until the 19th century
08:29
that scientists were able to detect
08:32
the stellar parallax.
08:34
So, there's a third problem as well.
08:35
The third problem is simply a factual problem,
08:38
that a lot of science doesn't fit the textbook model.
08:41
A lot of science isn't deductive at all,
08:43
it's actually inductive.
08:46
And by that we mean that scientists don't necessarily
08:48
start with theories and hypotheses,
08:50
often they just start with observations
08:52
of stuff going on in the world.
08:54
And the most famous example
of that is one of the most
08:57
famous scientists who ever lived, Charles Darwin.
08:59
When Darwin went out as a young
man on the voyage of the Beagle,
09:02
he didn't have a hypothesis, he didn't have a theory.
09:05
He just knew that he wanted
to have a career as a scientist
09:09
and he started to collect data.
09:12
Mainly he knew that he hated medicine
09:14
because the sight of blood made him sick so
09:17
he had to have an alternative career path.
09:19
So he started collecting data.
09:21
And he collected many things,
including his famous finches.
09:23
When he collected these finches,
he threw them in a bag
09:26
and he had no idea what they meant.
09:28
Many years later back in London,
09:31
Darwin looked at his data again and began
09:33
to develop an explanation,
09:35
and that explanation was the
theory of natural selection.
09:38
Besides inductive science,
09:41
scientists also often participate in modeling.
09:43
One of the things scientists want to do in life
09:46
is to explain the causes of things.
09:48
And how do we do that?
09:51
Well, one way you can do it is to build a model
09:52
that tests an idea.
09:54
So this is a picture of Henry Cadell,
09:56
who was a Scottish geologist in the 19th century.
09:58
You can tell he's Scottish because he's wearing
10:01
a deerstalker cap and Wellington boots.
10:02
(Laughter)
10:05
And Cadell wanted to answer the question,
10:07
how are mountains formed?
10:08
And one of the things he had observed
10:10
is that if you look at mountains
like the Appalachians,
10:12
you often find that the rocks in them
10:14
are folded,
10:16
and they're folded in a particular way,
10:17
which suggested to him
10:19
that they were actually being
compressed from the side.
10:20
And this idea would later play a major role
10:23
in discussions of continental drift.
10:25
So he built this model, this crazy contraption
10:28
with levers and wood, and here's his wheelbarrow,
10:30
buckets, a big sledgehammer.
10:33
I don't know why he's got the Wellington boots.
10:35
Maybe it's going to rain.
10:37
And he created this physical model in order
10:38
to demonstrate that you could, in fact, create
10:42
patterns in rocks, or at least, in this case, in mud,
10:46
that looked a lot like mountains
10:48
if you compressed them from the side.
10:50
So it was an argument about
the cause of mountains.
10:52
Nowadays, most scientists prefer to work inside,
10:56
so they don't build physical models so much
10:59
as to make computer simulations.
11:01
But a computer simulation is a kind of a model.
11:04
It's a model that's made with mathematics,
11:07
and like the physical models of the 19th century,
11:08
it's very important for thinking about causes.
11:12
So one of the big questions
to do with climate change,
11:15
we have tremendous amounts of evidence
11:18
that the Earth is warming up.
11:20
This slide here, the black line shows
11:22
the measurements that scientists have taken
11:24
for the last 150 years
11:26
showing that the Earth's temperature
11:28
has steadily increased,
11:30
and you can see in particular
that in the last 50 years
11:31
there's been this dramatic increase
11:34
of nearly one degree centigrade,
11:36
or almost two degrees Fahrenheit.
11:38
So what, though, is driving that change?
11:41
How can we know what's causing
11:43
the observed warming?
11:45
Well, scientists can model it
11:47
using a computer simulation.
11:49
So this diagram illustrates a computer simulation
11:51
that has looked at all the different factors
11:54
that we know can influence the Earth's climate,
11:56
so sulfate particles from air pollution,
11:59
volcanic dust from volcanic eruptions,
12:01
changes in solar radiation,
12:04
and, of course, greenhouse gases.
12:07
And they asked the question,
12:09
what set of variables put into a model
12:11
will reproduce what we actually see in real life?
12:14
So here is the real life in black.
12:17
Here's the model in this light gray,
12:19
and the answer is
12:22
a model that includes, it's the answer E on that SAT,
12:23
all of the above.
12:28
The only way you can reproduce
12:30
the observed temperature measurements
12:31
is with all of these things put together,
12:33
including greenhouse gases,
12:35
and in particular you can see that the increase
12:37
in greenhouse gases tracks
12:40
this very dramatic increase in temperature
12:42
over the last 50 years.
12:44
And so this is why climate scientists say
12:45
it's not just that we know that
climate change is happening,
12:48
we know that greenhouse gases are a major part
12:51
of the reason why.
12:54
So now because there all these different things
12:56
that scientists do,
12:59
the philosopher Paul Feyerabend famously said,
13:00
"The only principle in science
13:04
that doesn't inhibit progress is: anything goes."
13:05
Now this quotation has often
been taken out of context,
13:09
because Feyerabend was not actually saying
13:12
that in science anything goes.
13:14
What he was saying was,
13:16
actually the full quotation is,
13:17
"If you press me to say
13:19
what is the method of science,
13:21
I would have to say: anything goes."
13:23
What he was trying to say
13:27
is that scientists do a lot of different things.
13:28
Scientists are creative.
13:30
But then this pushes the question back:
13:33
If scientists don't use a single method,
13:35
then how do they decide
13:38
what's right and what's wrong?
13:40
And who judges?
13:42
And the answer is, scientists judge,
13:44
and they judge by judging evidence.
13:46
Scientists collect evidence in many different ways,
13:49
but however they collect it,
13:52
they have to subject it to scrutiny.
13:54
And this led the sociologist Robert Merton
13:56
to focus on this question of how scientists
13:59
scrutinize data and evidence,
14:01
and he said they do it in a way he called
14:03
"organized skepticism."
14:05
And by that he meant it's organized
14:07
because they do it collectively,
14:09
they do it as a group,
14:11
and skepticism, because they do it from a position
14:12
of distrust.
14:15
That is to say, the burden of proof
14:17
is on the person with a novel claim.
14:18
And in this sense, science
is intrinsically conservative.
14:21
It's quite hard to persuade the scientific community
14:24
to say, "Yes, we know something, this is true."
14:27
So despite the popularity of the concept
14:30
of paradigm shifts,
14:33
what we find is that actually,
14:34
really major changes in scientific thinking
14:36
are relatively rare in the history of science.
14:39
So finally that brings us to one more idea:
14:42
If scientists judge evidence collectively,
14:46
this has led historians to focus on the question
14:50
of consensus,
14:52
and to say that at the end of the day,
14:54
what science is,
14:55
what scientific knowledge is,
14:57
is the consensus of the scientific experts
14:59
who through this process of organized scrutiny,
15:02
collective scrutiny,
15:05
have judged the evidence
15:07
and come to a conclusion about it,
15:08
either yea or nay.
15:11
So we can think of scientific knowledge
15:13
as a consensus of experts.
15:15
We can also think of science as being
15:17
a kind of a jury,
15:19
except it's a very special kind of jury.
15:21
It's not a jury of your peers,
15:23
it's a jury of geeks.
15:25
It's a jury of men and women with Ph.D.s,
15:27
and unlike a conventional jury,
15:31
which has only two choices,
15:33
guilty or not guilty,
15:35
the scientific jury actually has a number of choices.
15:37
Scientists can say yes, something's true.
15:41
Scientists can say no, it's false.
15:44
Or, they can say, well it might be true
15:46
but we need to work more
and collect more evidence.
15:49
Or, they can say it might be true,
15:52
but we don't know how to answer the question
15:53
and we're going to put it aside
15:55
and maybe we'll come back to it later.
15:56
That's what scientists call "intractable."
15:59
But this leads us to one final problem:
16:03
If science is what scientists say it is,
16:06
then isn't that just an appeal to authority?
16:09
And weren't we all taught in school
16:11
that the appeal to authority is a logical fallacy?
16:13
Well, here's the paradox of modern science,
16:16
the paradox of the conclusion I think historians
16:19
and philosophers and sociologists have come to,
16:21
that actually science is the appeal to authority,
16:24
but it's not the authority of the individual,
16:27
no matter how smart that individual is,
16:31
like Plato or Socrates or Einstein.
16:33
It's the authority of the collective community.
16:37
You can think of it is a kind of wisdom of the crowd,
16:40
but a very special kind of crowd.
16:43
Science does appeal to authority,
16:47
but it's not based on any individual,
16:49
no matter how smart that individual may be.
16:51
It's based on the collective wisdom,
16:54
the collective knowledge, the collective work,
16:56
of all of the scientists who have worked
16:58
on a particular problem.
17:00
Scientists have a kind of culture of collective distrust,
17:03
this "show me" culture,
17:06
illustrated by this nice woman here
17:08
showing her colleagues her evidence.
17:10
Of course, these people don't
really look like scientists,
17:13
because they're much too happy.
17:15
(Laughter)
17:17
Okay, so that brings me to my final point.
17:21
Most of us get up in the morning.
17:25
Most of us trust our cars.
17:28
Well, see, now I'm thinking, I'm in Manhattan,
17:29
this is a bad analogy,
17:31
but most Americans who don't live in Manhattan
17:32
get up in the morning and get in their cars
17:35
and turn on that ignition, and their cars work,
17:37
and they work incredibly well.
17:39
The modern automobile hardly ever breaks down.
17:41
So why is that? Why do cars work so well?
17:44
It's not because of the genius of Henry Ford
17:47
or Karl Benz or even Elon Musk.
17:49
It's because the modern automobile
17:52
is the product of more than 100 years of work
17:54
by hundreds and thousands
17:59
and tens of thousands of people.
18:01
The modern automobile is the product
18:02
of the collected work and wisdom and experience
18:04
of every man and woman who has ever worked
18:07
on a car,
18:10
and the reliability of the technology is the result
18:11
of that accumulated effort.
18:14
We benefit not just from the genius of Benz
18:17
and Ford and Musk
18:20
but from the collective intelligence and hard work
18:21
of all of the people who have worked
18:23
on the modern car.
18:26
And the same is true of science,
18:27
only science is even older.
18:29
Our basis for trust in science is actually the same
18:32
as our basis in trust in technology,
18:35
and the same as our basis for trust in anything,
18:38
namely, experience.
18:42
But it shouldn't be blind trust
18:44
any more than we would have blind trust in anything.
18:46
Our trust in science, like science itself,
18:48
should be based on evidence,
18:51
and that means that scientists
18:53
have to become better communicators.
18:55
They have to explain to us not just what they know
18:57
but how they know it,
19:00
and it means that we have
to become better listeners.
19:01
Thank you very much.
19:05
(Applause)
19:07

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About the speaker:

Naomi Oreskes - Historian of science
Naomi 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.

More profile about the speaker
Naomi Oreskes | Speaker | TED.com