February 12, 2021 • 14 mins
We're no stranger to stories about misinformation or deliberate disinformation. We live in a world where now more than ever, you have to be skeptical. That skepticism can be healthy, but it also can be used to cast more doubt and misinformation on data and statistics that are very real. Tim Harford talks to Niala Boodhoo, from the news podcast Axios Today, about why people believe things that aren't true.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:15):
Pushkin. Hello, Cautionary Tales listeners. As I'm getting ready to
kick off my second season on the twenty sixth of February,
I wanted to share my appearance on a different show
from our friends at Pushkin and Axios. On this episode
(00:37):
of Axios Today, I talk with host Nila Boodoo about
my book, The Data Detective and why people believe things
that aren't true. We live in a world where, now
more than ever, you have to be skeptical. That skepticism
can be healthy, but it can also be used to
cast doubt on data and statistics that are very real,
(00:58):
and to spread misinformation. Listen to my conversation with Nila
and subscribe to Axios Today wherever you get your podcasts.
Here's the show. Tim Harford is a senior columnist with
The Financial Times, and he's also author of The Data Detective,
which is just out this week. Hi, Tim, welcome to
(01:19):
ax Yesterday. Oh thank you very much for having me.
So I have to first just ask you. The title
of this is how to make the World Add Up
outside the US, but it's The Data Detective here in
the US. Is this like a Harry Potter situation? Why
do we have a different title. Yeah, I'm hoping for
the Harry Potter sales. That would be nice. Yeah, it's
(01:39):
as simple as the UK publisher didn't like the US title.
The US publisher didn't like the UK title, and I
just have to explain it to everybody that I talked about. No,
I thought it had some greater significance, like statistically about
the way that Americans interpret words. It's exactly the same
book all over the world, just a different title. And
it's all about trying to help people think clearly about
(02:01):
the world, using, among other things, the tools of statistics.
And you're right that we might be at a bit
of a fork in the road or a moment when
it comes to statistics, particular when we think about the pandemic. Yeah,
I think so, because we've seen a tremendous amount of
misinformation and even deliberate disinformation, but we've also seen a
(02:26):
credible appreciation of just how life saving accurate numbers can be.
All of the questions we want answering, like, you know,
where is the virus, who's got it, how's it spreading?
What are the risky activities? Do masks help, what treatments work,
do the vaccines work? All of these life or death questions,
you can't answer any of them without good data. And
(02:49):
so I think people have started to appreciate that while
there is a lot of polarization, there is a lot
of misinformation, they are helping us make incredibly important and
consequential decisions. But of course, our societal problems and the
polarization aren't about the statistics themselves. They're about whether we
believe them. Today twenty one, the idea that statistics are
(03:10):
a lie is almost accepted fact. Yeah, although it's a
lot easier to lie without statistics. Let me tell you so.
I mean that idea goes back, way, way, way way back.
So the time of Mark Twain, people were talking about lies,
damned lies and statistics. But for me, at the moment
that I really identified as significant was nineteen fifty four.
(03:32):
Because two different things happened in nineteen fifty four in
the same year. You've got this, to me incredibly dramatic
illustration of these different views of statistics. There's this one guy,
Darryl Half, who wrote How to Lie with Statistics, who's saying, yeah,
it's like a stage magician's trick. You can never trust them.
(03:52):
It's fun to figure out how the trick is done,
and I'll show you how statistics are used to deceive you.
And then you've got these these two epidemiologists, Richard darl
and Austin Bradford Hill, who are saying this is not
a trick, this is life or death, and their discover
that smoking cigarettes dramatically increases your risk of lung cancer
(04:13):
has helped to save hundreds of millions of lives. It's
not not a game at all. The irony of that
bifurcation back in nineteen fifty three is that pretty soon
Darryl Half, the How to Lie with Statistics guy, ended
up testifying in front of Congress, basically saying where you
couldn't really believe all the statistics that showed you that
cigarettes were dangerous. So it was a very short trip
(04:37):
from here's a fun book exposing statistical fallacies. I'm standing
in front of Congress and I'm telling you that there's
no evidence that cigarettes are dangerous. It's pretty dark. And
how do you see that direct line from that moment
with casting doubt on scientists work when it comes to
tobacco and cancer, to climate science deniers or to what
(05:02):
we see now, Well, there's a well documented link. Now
that we've got more than four hundred thousand Americans dead
and my own country more than one hundred thousand BRIT's dead.
Now that you know those reassurances have been proved to
(05:23):
be false, the defense mechanism is to say, oh, well, look,
the scientist's got a load of stuff wrong as well.
So they'll, for example, points to the WHO, and they'll say,
the WHO told us that the infection fatality rate was
about three and a half percent at the beginning of
the pandemic, and nobody now thinks it's three and a
half percent. It's it's below one percent. But that smear
(05:45):
on the WHO is actually a deliberate distortion. Who never
said that the infection fatality rate was three point five percent.
They said something else was three point five percent the
case fatality rate, and that difference doesn't really matter. What
I think is interesting is you've got that same tactic
being used, which is I've been caught out, I've been discredited,
(06:07):
and I'm going to lash out and others the scientists
and claim that they've got stuff wrong that in fact
they haven't. Why is it easier to discredit arguments? Then
it's almost like an easier fight to discredit something than
to support something and prove it right. There are so
many ways to answer that question. I think, really, I'm
(06:27):
not sure why it's easier, but it is easier, and
we've got good evidence that it's easier. I mean, you
just have to look around at the preponderance of negative campaigning,
for example. But we've got somebody nice evidence in experiments
conducted by political scientists and psychologists. So there's one from
the mid nineties that just showed people a bunch of
(06:49):
arguments about real hot button issues like the death penalty,
gun control, abortion rights, the sorts of things that people
get really heated about and feel very passionately about, and
they ask people to evaluate the strengths and weaknesses of
these different political arguments. And what the research has found was, Yeah,
(07:09):
people find it a lot easier to come up with
arguments supporting what they already believe, and they find it
harder to come up with arguments supporting the opposing point
of view. But they found that that's doubly true when
it comes to negative arguments. People found it incredibly easy
to produce negative arguments reasons to disbelieve the political positions
(07:34):
that they disagreed with. And I think that that's behind
the tobacco strategy, the climate change strategy, now the COVID
denial strategy, the same basic approach. If you don't want
to believe this, it's very easy for me to give
you reasons to doubt, very easy. Indeed, doubt has this
special kind of power, it seems, it's very tempting. Even
for people who really respect the numbers and respect evidence.
(07:56):
It's easy for us to fall into the trap of
constantly focusing on errors and mistakes, and that, I think
just feeds into this narrative that the numbers are always lying,
that they'll never tell you anything useful, and that's just
not right. So how do we, for example, for someone
like you who love statistics, obviously, how do we not
(08:16):
take numbers for granted? I think, just to notice how
important they have been in the pandemic. The metaphor for me,
it's like radar. So when we developed radar in the
late nineteen thirties, that turned out to be an incredibly
important innovation in the UK. It helped us turn back
(08:39):
the German Luftwaffe. Then we took radar technology, took it
to the States and the United States poured an incredible
amount of money into perfecting radar and perfecting that technology
because it's just so incredibly important to be able to
see what's coming at you. And for me, statistics are
like that. They're showing us the threats, they're showing us
(08:59):
the weaknesses on our own system. They're showing us where
policy is working and where policy is failing to work. Now,
where the supplies of PPE are going, where the supplies
are accina going, who is suffering most, and who needs support?
All of these things. You've got no chance of figuring
out any of this stuff without good statistics, without good data.
(09:20):
And so it frustrates me when we sit around going,
oh yeah, lies down, lies and statistics, and we treat
it as though it's just a weapon in a political argument,
and it's so much more important than that and so
much more useful. I think part of this is just
the natural. Also, we've talked about the human nature and
just sort of our tendency to doubt. I think a
lot of us also is how overwhelmed we are with
(09:43):
the amount of information and statistics that are coming at us.
And so how do you personally manage that? How do
you keep from being overwhelmed with information and statistics. The
first piece of advice that I give in the book
I think has surprised quite a lot of people. It's
nothing to do with technical tips on correlations or our
(10:06):
squared or sampling by so any of that stuff. I
just say, whenever you see a claim, statistical claim, a
newspaper headline, ask yourself how you are feeling when you
see that. Ask yourself what your emotional reaction is to
the claim. Because so many media headlines, so many social
(10:28):
media posts, are designed to arouse an emotional reaction. That's
kind of that's what makes a good newspaper headline, that's
what gets the clicks, that's what gets the shares and
the likes. But if you're processing information and you're in
an emotionally hot state, you're feeling angry, you're feeling vindicated, joyful,
any emotion at all, you're not thinking clearly. So my
(10:50):
advice is just you don't even need to count to ten.
Just count to three, notice your emotional reaction, and then
go back and look at the claim a second time,
and you'll already be thinking in a calmer and clearer way.
I know I sound like some yoga instruction that when's
the last time you do that? Well, I do that
(11:12):
all the time. It's like a It's a total habit
of mind for me, because I'm as vulnerable to emotional
thinking as anybody else. But it's just a complete reflex.
When I'm on Twitter, and Twitter is a place where
there's a lot of angry stuff going on. The moment
I see something and I'm minded to retweet it, to comment,
(11:33):
to share, I just hang on a moment, just notice
my own reaction to it, and then I may, of
course go on and share it. But I've already started
to spot the potential errors and the ways in which
it's not just that other people are fooling me, it's
that I am fooling myself. And I'm always going to
keep fooling myself if I'm feeling highly emotional when I
(11:56):
see these claims. I mean, you have talked about this.
We have talked about this many times on the podcast
Acts Us today, and I'm sure anyone who is even
a mild consumer of news is aware about the idea
of checking your sources right, checking your emotions. So I
think I wonder if you feel like that's just the
(12:16):
world that we live in now, where we have to
remember to be vigilant about all of these things, knowing
that people are probably exhausted of being vigilant about a
lot of other things. Yes, I mean, it would be
nice if every journalist, if everyone who ever posted on
social media did all that work for us, put everything
into context, gave all the sources linked us to complementary
(12:41):
or opposing points of view, so we could really sort
of evaluate everything. And the best journalists do the best
sources really do that. But if they're not going to
do it, you need to at least be aware that
someone is trying to get you to feel something. But
it's important to be vigilant and to be skeptical about
(13:02):
the news stories that we consume. I think it's just
as important to be vigilant and skeptical about our own
filters and biases, because you can consume a diet of
really excellent news and information, but if you're constantly processing
it in a very biased way, if you're really yearning
to each particular conclusion, you're still going to come out
thinking the wrong things. Can I end by just asking
(13:24):
what is the what is the one thing you want
people to take away from your book. If you are
curious about the world and you want to understand what
you're being told and how it fits into a bigger picture,
it's not that hard. Ask the right questions. Be open minded,
(13:46):
not too open minded, but be open minded and ask
whether what you're being told is making you smarter. When
you view information like that, rather than as a weapon
that might help you win some stupid argument, you're going
to be smarter about the world. Sim Harford is the
author of The Data Detective, which is out this week
in the US, and you can also catch him on
(14:08):
the Cautionary Tales podcast produced by our partners at Pushkin.
Tim thanks very much for being with us. I appreciate it.
Thank you, thank you.
Pushkin. Hello, Cautionary Tales listeners. As I'm getting ready to
kick off my second season on the twenty sixth of February,
I wanted to share my appearance on a different show
from our friends at Pushkin and Axios. On this episode
(00:37):
of Axios Today, I talk with host Nila Boodoo about
my book, The Data Detective and why people believe things
that aren't true. We live in a world where, now
more than ever, you have to be skeptical. That skepticism
can be healthy, but it can also be used to
cast doubt on data and statistics that are very real,
(00:58):
and to spread misinformation. Listen to my conversation with Nila
and subscribe to Axios Today wherever you get your podcasts.
Here's the show. Tim Harford is a senior columnist with
The Financial Times, and he's also author of The Data Detective,
which is just out this week. Hi, Tim, welcome to
(01:19):
ax Yesterday. Oh thank you very much for having me.
So I have to first just ask you. The title
of this is how to make the World Add Up
outside the US, but it's The Data Detective here in
the US. Is this like a Harry Potter situation? Why
do we have a different title. Yeah, I'm hoping for
the Harry Potter sales. That would be nice. Yeah, it's
(01:39):
as simple as the UK publisher didn't like the US title.
The US publisher didn't like the UK title, and I
just have to explain it to everybody that I talked about. No,
I thought it had some greater significance, like statistically about
the way that Americans interpret words. It's exactly the same
book all over the world, just a different title. And
it's all about trying to help people think clearly about
(02:01):
the world, using, among other things, the tools of statistics.
And you're right that we might be at a bit
of a fork in the road or a moment when
it comes to statistics, particular when we think about the pandemic. Yeah,
I think so, because we've seen a tremendous amount of
misinformation and even deliberate disinformation, but we've also seen a
(02:26):
credible appreciation of just how life saving accurate numbers can be.
All of the questions we want answering, like, you know,
where is the virus, who's got it, how's it spreading?
What are the risky activities? Do masks help, what treatments work,
do the vaccines work? All of these life or death questions,
you can't answer any of them without good data. And
(02:49):
so I think people have started to appreciate that while
there is a lot of polarization, there is a lot
of misinformation, they are helping us make incredibly important and
consequential decisions. But of course, our societal problems and the
polarization aren't about the statistics themselves. They're about whether we
believe them. Today twenty one, the idea that statistics are
(03:10):
a lie is almost accepted fact. Yeah, although it's a
lot easier to lie without statistics. Let me tell you so.
I mean that idea goes back, way, way, way way back.
So the time of Mark Twain, people were talking about lies,
damned lies and statistics. But for me, at the moment
that I really identified as significant was nineteen fifty four.
(03:32):
Because two different things happened in nineteen fifty four in
the same year. You've got this, to me incredibly dramatic
illustration of these different views of statistics. There's this one guy,
Darryl Half, who wrote How to Lie with Statistics, who's saying, yeah,
it's like a stage magician's trick. You can never trust them.
(03:52):
It's fun to figure out how the trick is done,
and I'll show you how statistics are used to deceive you.
And then you've got these these two epidemiologists, Richard darl
and Austin Bradford Hill, who are saying this is not
a trick, this is life or death, and their discover
that smoking cigarettes dramatically increases your risk of lung cancer
(04:13):
has helped to save hundreds of millions of lives. It's
not not a game at all. The irony of that
bifurcation back in nineteen fifty three is that pretty soon
Darryl Half, the How to Lie with Statistics guy, ended
up testifying in front of Congress, basically saying where you
couldn't really believe all the statistics that showed you that
cigarettes were dangerous. So it was a very short trip
(04:37):
from here's a fun book exposing statistical fallacies. I'm standing
in front of Congress and I'm telling you that there's
no evidence that cigarettes are dangerous. It's pretty dark. And
how do you see that direct line from that moment
with casting doubt on scientists work when it comes to
tobacco and cancer, to climate science deniers or to what
(05:02):
we see now, Well, there's a well documented link. Now
that we've got more than four hundred thousand Americans dead
and my own country more than one hundred thousand BRIT's dead.
Now that you know those reassurances have been proved to
(05:23):
be false, the defense mechanism is to say, oh, well, look,
the scientist's got a load of stuff wrong as well.
So they'll, for example, points to the WHO, and they'll say,
the WHO told us that the infection fatality rate was
about three and a half percent at the beginning of
the pandemic, and nobody now thinks it's three and a
half percent. It's it's below one percent. But that smear
(05:45):
on the WHO is actually a deliberate distortion. Who never
said that the infection fatality rate was three point five percent.
They said something else was three point five percent the
case fatality rate, and that difference doesn't really matter. What
I think is interesting is you've got that same tactic
being used, which is I've been caught out, I've been discredited,
(06:07):
and I'm going to lash out and others the scientists
and claim that they've got stuff wrong that in fact
they haven't. Why is it easier to discredit arguments? Then
it's almost like an easier fight to discredit something than
to support something and prove it right. There are so
many ways to answer that question. I think, really, I'm
(06:27):
not sure why it's easier, but it is easier, and
we've got good evidence that it's easier. I mean, you
just have to look around at the preponderance of negative campaigning,
for example. But we've got somebody nice evidence in experiments
conducted by political scientists and psychologists. So there's one from
the mid nineties that just showed people a bunch of
(06:49):
arguments about real hot button issues like the death penalty,
gun control, abortion rights, the sorts of things that people
get really heated about and feel very passionately about, and
they ask people to evaluate the strengths and weaknesses of
these different political arguments. And what the research has found was, Yeah,
(07:09):
people find it a lot easier to come up with
arguments supporting what they already believe, and they find it
harder to come up with arguments supporting the opposing point
of view. But they found that that's doubly true when
it comes to negative arguments. People found it incredibly easy
to produce negative arguments reasons to disbelieve the political positions
(07:34):
that they disagreed with. And I think that that's behind
the tobacco strategy, the climate change strategy, now the COVID
denial strategy, the same basic approach. If you don't want
to believe this, it's very easy for me to give
you reasons to doubt, very easy. Indeed, doubt has this
special kind of power, it seems, it's very tempting. Even
for people who really respect the numbers and respect evidence.
(07:56):
It's easy for us to fall into the trap of
constantly focusing on errors and mistakes, and that, I think
just feeds into this narrative that the numbers are always lying,
that they'll never tell you anything useful, and that's just
not right. So how do we, for example, for someone
like you who love statistics, obviously, how do we not
(08:16):
take numbers for granted? I think, just to notice how
important they have been in the pandemic. The metaphor for me,
it's like radar. So when we developed radar in the
late nineteen thirties, that turned out to be an incredibly
important innovation in the UK. It helped us turn back
(08:39):
the German Luftwaffe. Then we took radar technology, took it
to the States and the United States poured an incredible
amount of money into perfecting radar and perfecting that technology
because it's just so incredibly important to be able to
see what's coming at you. And for me, statistics are
like that. They're showing us the threats, they're showing us
(08:59):
the weaknesses on our own system. They're showing us where
policy is working and where policy is failing to work. Now,
where the supplies of PPE are going, where the supplies
are accina going, who is suffering most, and who needs support?
All of these things. You've got no chance of figuring
out any of this stuff without good statistics, without good data.
(09:20):
And so it frustrates me when we sit around going,
oh yeah, lies down, lies and statistics, and we treat
it as though it's just a weapon in a political argument,
and it's so much more important than that and so
much more useful. I think part of this is just
the natural. Also, we've talked about the human nature and
just sort of our tendency to doubt. I think a
lot of us also is how overwhelmed we are with
(09:43):
the amount of information and statistics that are coming at us.
And so how do you personally manage that? How do
you keep from being overwhelmed with information and statistics. The
first piece of advice that I give in the book
I think has surprised quite a lot of people. It's
nothing to do with technical tips on correlations or our
(10:06):
squared or sampling by so any of that stuff. I
just say, whenever you see a claim, statistical claim, a
newspaper headline, ask yourself how you are feeling when you
see that. Ask yourself what your emotional reaction is to
the claim. Because so many media headlines, so many social
(10:28):
media posts, are designed to arouse an emotional reaction. That's
kind of that's what makes a good newspaper headline, that's
what gets the clicks, that's what gets the shares and
the likes. But if you're processing information and you're in
an emotionally hot state, you're feeling angry, you're feeling vindicated, joyful,
any emotion at all, you're not thinking clearly. So my
(10:50):
advice is just you don't even need to count to ten.
Just count to three, notice your emotional reaction, and then
go back and look at the claim a second time,
and you'll already be thinking in a calmer and clearer way.
I know I sound like some yoga instruction that when's
the last time you do that? Well, I do that
(11:12):
all the time. It's like a It's a total habit
of mind for me, because I'm as vulnerable to emotional
thinking as anybody else. But it's just a complete reflex.
When I'm on Twitter, and Twitter is a place where
there's a lot of angry stuff going on. The moment
I see something and I'm minded to retweet it, to comment,
(11:33):
to share, I just hang on a moment, just notice
my own reaction to it, and then I may, of
course go on and share it. But I've already started
to spot the potential errors and the ways in which
it's not just that other people are fooling me, it's
that I am fooling myself. And I'm always going to
keep fooling myself if I'm feeling highly emotional when I
(11:56):
see these claims. I mean, you have talked about this.
We have talked about this many times on the podcast
Acts Us today, and I'm sure anyone who is even
a mild consumer of news is aware about the idea
of checking your sources right, checking your emotions. So I
think I wonder if you feel like that's just the
(12:16):
world that we live in now, where we have to
remember to be vigilant about all of these things, knowing
that people are probably exhausted of being vigilant about a
lot of other things. Yes, I mean, it would be
nice if every journalist, if everyone who ever posted on
social media did all that work for us, put everything
into context, gave all the sources linked us to complementary
(12:41):
or opposing points of view, so we could really sort
of evaluate everything. And the best journalists do the best
sources really do that. But if they're not going to
do it, you need to at least be aware that
someone is trying to get you to feel something. But
it's important to be vigilant and to be skeptical about
(13:02):
the news stories that we consume. I think it's just
as important to be vigilant and skeptical about our own
filters and biases, because you can consume a diet of
really excellent news and information, but if you're constantly processing
it in a very biased way, if you're really yearning
to each particular conclusion, you're still going to come out
thinking the wrong things. Can I end by just asking
(13:24):
what is the what is the one thing you want
people to take away from your book. If you are
curious about the world and you want to understand what
you're being told and how it fits into a bigger picture,
it's not that hard. Ask the right questions. Be open minded,
(13:46):
not too open minded, but be open minded and ask
whether what you're being told is making you smarter. When
you view information like that, rather than as a weapon
that might help you win some stupid argument, you're going
to be smarter about the world. Sim Harford is the
author of The Data Detective, which is out this week
in the US, and you can also catch him on
(14:08):
the Cautionary Tales podcast produced by our partners at Pushkin.
Tim thanks very much for being with us. I appreciate it.
Thank you, thank you.
Host
Tim Harford
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