Why you should love statistics
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0:01 - 0:04Back in 2003,
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0:04 - 0:06the UK government carried out a survey.
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0:07 - 0:11And it was a survey that measured
levels of numeracy -
0:11 - 0:12in the population.
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0:12 - 0:14And they were shocked to find out
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0:14 - 0:17that for every 100 working age
adults in the country, -
0:17 - 0:2047 of them lacked Level 1 numeracy skills.
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0:21 - 0:25Now, Level 1 numeracy skills --
that's low-end GCSE score. -
0:25 - 0:29It's the ability to deal with fractions,
percentages and decimals. -
0:29 - 0:33So this figure prompted
a lot of hand-wringing in Whitehall. -
0:33 - 0:35Policies were changed,
-
0:35 - 0:37investments were made,
-
0:37 - 0:40and then they ran
the survey again in 2011. -
0:40 - 0:42So can you guess
what happened to this number? -
0:44 - 0:45It went up to 49.
-
0:45 - 0:47(Laughter)
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0:47 - 0:49And in fact, when I reported
this figure in the FT, -
0:49 - 0:51one of our readers joked and said,
-
0:51 - 0:55"This figure is only shocking
to 51 percent of the population." -
0:55 - 0:57(Laughter)
-
0:57 - 1:00But I preferred, actually,
the reaction of a schoolchild -
1:00 - 1:04when I presented
at a school this information, -
1:04 - 1:05who raised their hand and said,
-
1:05 - 1:08"How do we know that the person
who made that number -
1:08 - 1:09isn't one of the 49 percent either?"
-
1:09 - 1:11(Laughter)
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1:11 - 1:15So clearly, there's a numeracy issue,
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1:15 - 1:17because these are
important skills for life, -
1:17 - 1:21and a lot of the changes
that we want to introduce in this century -
1:21 - 1:23involve us becoming
more comfortable with numbers. -
1:23 - 1:25Now, it's not just an English problem.
-
1:25 - 1:30OECD this year released some figures
looking at numeracy in young people, -
1:30 - 1:33and leading the way, the USA --
-
1:33 - 1:38nearly 40 percent of young people
in the US have low numeracy. -
1:38 - 1:39Now, England is there too,
-
1:39 - 1:44but there are seven OECD countries
with figures above 20 percent. -
1:45 - 1:47That is a problem,
because it doesn't have to be that way. -
1:47 - 1:49If you look at the far end of this graph,
-
1:49 - 1:52you can see the Netherlands and Korea
are in single figures. -
1:52 - 1:57So there's definitely a numeracy
problem that we want to address. -
1:58 - 2:00Now, as useful as studies like these are,
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2:00 - 2:06I think we risk herding people
inadvertently into one of two categories; -
2:06 - 2:08that there are two kinds of people:
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2:08 - 2:12those people that are comfortable
with numbers, that can do numbers, -
2:12 - 2:14and the people who can't.
-
2:14 - 2:16And what I'm trying
to talk about here today -
2:16 - 2:19is to say that I believe
that is a false dichotomy. -
2:20 - 2:21It's not an immutable pairing.
-
2:21 - 2:25I think you don't have to have
tremendously high levels of numeracy -
2:25 - 2:27to be inspired by numbers,
-
2:27 - 2:30and that should be the starting point
to the journey ahead. -
2:30 - 2:35And one of the ways in which
we can begin that journey, for me, -
2:35 - 2:36is looking at statistics.
-
2:36 - 2:40Now, I am the first to acknowledge
that statistics has got somewhat -
2:40 - 2:41of an image problem.
-
2:41 - 2:42(Laughter)
-
2:42 - 2:44It's the part of mathematics
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2:44 - 2:47that even mathematicians
don't particularly like, -
2:47 - 2:51because whereas the rest of maths
is all about precision and certainty, -
2:51 - 2:53statistics is almost the reverse of that.
-
2:54 - 2:58But actually, I was a late convert
to the world of statistics myself. -
2:58 - 3:01If you'd asked my undergraduate professors
-
3:01 - 3:05what two subjects would I be least likely
to excel in after university, -
3:05 - 3:08they'd have told you statistics
and computer programming, -
3:08 - 3:11and yet here I am, about to show you
some statistical graphics -
3:11 - 3:12that I programmed.
-
3:13 - 3:14So what inspired that change in me?
-
3:15 - 3:18What made me think that statistics
was actually an interesting thing? -
3:18 - 3:20It's really because
statistics are about us. -
3:21 - 3:23If you look at the etymology
of the word statistics, -
3:23 - 3:26it's the science of dealing with data
-
3:26 - 3:29about the state or the community
that we live in. -
3:29 - 3:32So statistics are about us as a group,
-
3:32 - 3:34not us as individuals.
-
3:34 - 3:35And I think as social animals,
-
3:35 - 3:39we share this fascination about how
we as individuals relate to our groups, -
3:39 - 3:40to our peers.
-
3:41 - 3:44And statistics in this way
are at their most powerful -
3:44 - 3:45when they surprise us.
-
3:45 - 3:49And there's been some really wonderful
surveys carried out recently -
3:49 - 3:50by Ipsos MORI in the last few years.
-
3:50 - 3:53They did a survey of over
1,000 adults in the UK, -
3:53 - 3:57and said, for every 100 people
in England and Wales, -
3:57 - 3:59how many of them are Muslim?
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3:59 - 4:02Now the average answer from this survey,
-
4:02 - 4:05which was supposed to be representative
of the total population, was 24. -
4:05 - 4:09That's what people thought.
-
4:09 - 4:12British people think 24 out of every 100
people in the country are Muslim. -
4:12 - 4:17Now, official figures reveal
that figure to be about five. -
4:18 - 4:22So there's this big variation
between what we think, our perception, -
4:22 - 4:24and the reality as given by statistics.
-
4:24 - 4:25And I think that's interesting.
-
4:25 - 4:29What could possibly be causing
that misperception? -
4:29 - 4:31And I was so thrilled with this study,
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4:31 - 4:35I started to take questions out
in presentations. I was referring to it. -
4:35 - 4:36Now, I did a presentation
-
4:36 - 4:38at St. Paul's School for Girls
in Hammersmith, -
4:38 - 4:40and I had an audience rather like this,
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4:40 - 4:44except it was comprised entirely
of sixth-form girls. -
4:44 - 4:47And I said, "Girls,
-
4:48 - 4:52how many teenage girls do you think
the British public think -
4:52 - 4:54get pregnant every year?"
-
4:54 - 4:57And the girls were apoplectic when I said
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4:57 - 5:01the British public think that 15
out of every 100 teenage girls -
5:01 - 5:03get pregnant in the year.
-
5:03 - 5:06And they had every right to be angry,
-
5:06 - 5:08because in fact, I'd have to have
closer to 200 dots -
5:08 - 5:10before I could color one in,
-
5:10 - 5:13in terms of what
the official figures tell us. -
5:13 - 5:16And rather like numeracy,
this is not just an English problem. -
5:16 - 5:21Ipsos MORI expanded the survey
in recent years to go across the world. -
5:21 - 5:24And so, they asked Saudi Arabians,
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5:24 - 5:26for every 100 adults in your country,
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5:26 - 5:29how many of them are overweight or obese?
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5:31 - 5:36And the average answer from the Saudis
was just over a quarter. -
5:36 - 5:38That's what they thought.
-
5:38 - 5:40Just over a quarter of adults
are overweight or obese. -
5:40 - 5:45The official figures show, actually,
it's nearer to three-quarters. -
5:45 - 5:46(Laughter)
-
5:47 - 5:49So again, a big variation.
-
5:49 - 5:53And I love this one: they asked in Japan,
they asked the Japanese, -
5:53 - 5:55for every 100 Japanese people,
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5:55 - 5:58how many of them live in rural areas?
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5:59 - 6:03The average was about a 50-50 split,
just over halfway. -
6:03 - 6:08They thought 56 out of every 100
Japanese people lived in rural areas. -
6:08 - 6:09The official figure is seven.
-
6:10 - 6:15So extraordinary variations,
and surprising to some, -
6:15 - 6:17but not surprising to people
who have read the work -
6:17 - 6:22of Daniel Kahneman, for example,
the Nobel-winning economist. -
6:22 - 6:27He and his colleague, Amos Tversky,
spent years researching this disjoint -
6:27 - 6:30between what people perceive
and the reality, -
6:30 - 6:34the fact that people are actually
pretty poor intuitive statisticians. -
6:34 - 6:35And there are many reasons for this.
-
6:35 - 6:39Individual experiences, certainly,
can influence our perceptions, -
6:39 - 6:43but so, too, can things like the media
reporting things by exception, -
6:43 - 6:44rather than what's normal.
-
6:45 - 6:47Kahneman had a nice way
of referring to that. -
6:47 - 6:49He said, "We can be blind
to the obvious" -- -
6:49 - 6:51so we've got the numbers wrong --
-
6:51 - 6:53"but we can be blind
to our blindness about it." -
6:53 - 6:56And that has enormous
repercussions for decision making. -
6:56 - 6:59So at the statistics office
while this was all going on, -
6:59 - 7:01I thought this was really interesting.
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7:01 - 7:03I said, this is clearly a global problem,
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7:03 - 7:06but maybe geography is the issue here.
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7:06 - 7:10These were questions that were all about,
how well do you know your country? -
7:10 - 7:14So in this case, it's how well
do you know 64 million people? -
7:14 - 7:16Not very well, it turns out.
I can't do that. -
7:16 - 7:18So I had an idea,
-
7:18 - 7:21which was to think about
this same sort of approach -
7:21 - 7:23but to think about it
in a very local sense. -
7:23 - 7:24Is this a local?
-
7:24 - 7:26If we reframe the questions and say,
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7:26 - 7:28how well do you know your local area,
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7:28 - 7:30would your answers be any more accurate?
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7:32 - 7:34So I devised a quiz:
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7:34 - 7:35How well do you know your area?
-
7:36 - 7:38It's a simple Web app.
-
7:38 - 7:40You put in a post code
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7:40 - 7:42and then it will ask you questions
based on census data -
7:42 - 7:44for your local area.
-
7:44 - 7:46And I was very conscious
in designing this. -
7:46 - 7:51I wanted to make it open
to the widest possible range of people, -
7:51 - 7:53not just the 49 percent
who can get the numbers. -
7:53 - 7:55I wanted everyone to engage with it.
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7:55 - 7:57So for the design of the quiz,
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7:57 - 8:00I was inspired by the isotypes
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8:00 - 8:03of Otto Neurath from the 1920s and '30s.
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8:03 - 8:07Now, these are methods
for representing numbers -
8:07 - 8:09using repeating icons.
-
8:10 - 8:13And the numbers are there,
but they sit in the background. -
8:13 - 8:16So it's a great way
of representing quantity -
8:16 - 8:19without resorting to using terms
like "percentage," -
8:19 - 8:20"fractions" and "ratios."
-
8:20 - 8:22So here's the quiz.
-
8:22 - 8:24The layout of the quiz is,
-
8:24 - 8:27you have your repeating icons
on the left-hand side there, -
8:27 - 8:30and a map showing you the area
we're asking you questions about -
8:30 - 8:31on the right-hand side.
-
8:31 - 8:32There are seven questions.
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8:32 - 8:36Each question, there's a possible answer
between zero and a hundred, -
8:36 - 8:38and at the end of the quiz,
-
8:38 - 8:41you get an overall score
between zero and a hundred. -
8:41 - 8:43And so because this is TEDxExeter,
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8:43 - 8:45I thought we would have
a quick look at the quiz -
8:45 - 8:48for the first few questions of Exeter.
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8:48 - 8:49And so the first question is:
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8:49 - 8:52For every 100 people,
how many are aged under 16? -
8:53 - 8:56Now, I don't know Exeter very well
at all, so I had a guess at this, -
8:56 - 8:59but it gives you an idea
of how this quiz works. -
8:59 - 9:03You drag the slider
to highlight your icons, -
9:03 - 9:05and then just click "Submit" to answer,
-
9:05 - 9:09and we animate away the difference
between your answer and reality. -
9:09 - 9:13And it turns out, I was a pretty
terrible guess: five. -
9:13 - 9:15How about the next question?
-
9:15 - 9:17This is asking about
what the average age is, -
9:17 - 9:19so the age at which half
the population are younger -
9:19 - 9:21and half the population are older.
-
9:21 - 9:24And I thought 35 -- that sounds
middle-aged to me. -
9:24 - 9:26(Laughter)
-
9:28 - 9:30Actually, in Exeter,
it's incredibly young, -
9:30 - 9:35and I had underestimated the impact
of the university in this area. -
9:35 - 9:37The questions get harder
as you go through. -
9:37 - 9:39So this one's now asking
about homeownership: -
9:40 - 9:44For every 100 households, how many
are owned with a mortgage or loan? -
9:44 - 9:45And I hedged my bets here,
-
9:45 - 9:48because I didn't want to be
more than 50 out on the answer. -
9:48 - 9:50(Laughter)
-
9:50 - 9:53And actually, these get harder,
these questions, -
9:53 - 9:55because when you're in an area,
when you're in a community, -
9:56 - 10:01things like age -- there are clues
to whether a population is old or young. -
10:01 - 10:03Just by looking around
the area, you can see it. -
10:03 - 10:07Something like homeownership
is much more difficult to see, -
10:07 - 10:09so we revert to our own heuristics,
-
10:09 - 10:14our own biases about how many people
we think own their own homes. -
10:14 - 10:17Now the truth is,
when we published this quiz, -
10:17 - 10:21the census data that it's based on
was already a few years old. -
10:21 - 10:24We've had online applications
that allow you to put in a post code -
10:25 - 10:27and get statistics back for years.
-
10:27 - 10:28So in some senses,
-
10:28 - 10:31this was all a little bit old
and not necessarily new. -
10:31 - 10:35But I was interested to see
what reaction we might get -
10:35 - 10:38by game-ifying the data
in the way that we have, -
10:38 - 10:39by using animation
-
10:39 - 10:43and playing on the fact
that people have their own preconceptions. -
10:44 - 10:47It turns out, the reaction was, um ...
-
10:48 - 10:50was more than I could have hoped for.
-
10:50 - 10:54It was a long-held ambition of mine
to bring down a statistics website -
10:54 - 10:55due to public demand.
-
10:55 - 10:57(Laughter)
-
10:57 - 11:00This URL contains the words
"statistics," "gov" and "UK," -
11:00 - 11:04which are three of people's least
favorite words in a URL. -
11:04 - 11:08And the amazing thing about this
was that the website came down -
11:08 - 11:10at quarter to 10 at night,
-
11:10 - 11:13because people were actually
engaging with this data -
11:13 - 11:15of their own free will,
-
11:15 - 11:17using their own personal time.
-
11:17 - 11:19I was very interested to see
-
11:19 - 11:23that we got something like
a quarter of a million people -
11:23 - 11:26playing the quiz within the space
of 48 hours of launching it. -
11:26 - 11:30And it sparked an enormous discussion
online, on social media, -
11:30 - 11:32which was largely dominated
-
11:32 - 11:36by people having fun
with their misconceptions, -
11:36 - 11:39which is something that
I couldn't have hoped for any better, -
11:39 - 11:40in some respects.
-
11:41 - 11:44I also liked the fact that people started
sending it to politicians. -
11:44 - 11:46How well do you know the area
you claim to represent? -
11:46 - 11:48(Laughter)
-
11:48 - 11:49And then just to finish,
-
11:50 - 11:52going back to the two kinds of people,
-
11:52 - 11:55I thought it would be
really interesting to see -
11:55 - 11:57how people who are good with numbers
would do on this quiz. -
11:57 - 12:00The national statistician
of England and Wales, John Pullinger, -
12:01 - 12:03you would expect he would be pretty good.
-
12:04 - 12:06He got 44 for his own area.
-
12:06 - 12:08(Laughter)
-
12:08 - 12:13Jeremy Paxman -- admittedly,
after a glass of wine -- 36. -
12:14 - 12:16Even worse.
-
12:16 - 12:19It just shows you that the numbers
can inspire us all. -
12:19 - 12:20They can surprise us all.
-
12:20 - 12:22So very often, we talk about statistics
-
12:22 - 12:24as being the science of uncertainty.
-
12:24 - 12:26My parting thought for today is:
-
12:26 - 12:29actually, statistics is the science of us.
-
12:29 - 12:32And that's why we should
be fascinated by numbers. -
12:32 - 12:33Thank you very much.
-
12:33 - 12:37(Applause)
- Title:
- Why you should love statistics
- Speaker:
- Alan Smith
- Description:
-
Think you're good at guessing stats? Guess again. Whether we consider ourselves math people or not, our ability to understand and work with numbers is terribly limited, says data visualization expert Alan Smith. In this delightful talk, Smith demos a custom app he developed to help people discover the mismatch between what we know and what we think we know.
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDTalks
- Duration:
- 12:49
Brian Greene edited English subtitles for Why we're so bad at statistics | ||
Krystian Aparta edited English subtitles for Why we're so bad at statistics | ||
Brian Greene edited English subtitles for Why we're so bad at statistics | ||
Brian Greene edited English subtitles for Why we're so bad at statistics | ||
Brian Greene edited English subtitles for Why we're so bad at statistics | ||
Camille Martínez accepted English subtitles for Why we're so bad at statistics | ||
Camille Martínez edited English subtitles for Why we're so bad at statistics | ||
Camille Martínez edited English subtitles for Why we're so bad at statistics |