TEDxExeter
Alan Smith: Why you should love statistics
艾伦·史密斯: 为什么应该热衷于统计学
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你认为自己擅长猜数字？再好好想想吧。数据视觉化专家艾伦·史密斯说，不管我们认为自己是否精通数学，我们理解和运用数字的能力都是非常有限的。在这个气氛轻松的演讲中，史密斯探索了我们知道的和我们自以为知道的事情之间的差异。
Alan Smith - Data visualisation editor
Alan Smith uses interactive graphics and statistics to breathe new life into how data is presented. Full bio
Alan Smith uses interactive graphics and statistics to breathe new life into how data is presented. Full bio
Double-click the English transcript below to play the video.
00:12
Back in 2003,
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早在2003年
英国政府进行了一项调查
00:15
the UK government carried out a survey.
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目的是了解国民的
00:19
And it was a survey that measured
levels of numeracy
levels of numeracy
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00:22
in the population.
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算术能力
00:23
And they were shocked to find out
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结果非常让人吃惊
00:25
that for every 100 working age
adults in the country,
adults in the country,
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在英国，每100个正值
工作年纪的成人中
工作年纪的成人中
47个缺乏一级的算术能力
00:28
47 of them lacked Level 1 numeracy skills.
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00:32
Now, Level 1 numeracy skills --
that's low-end GCSE score.
that's low-end GCSE score.
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一级算术能力
是GSCE考试的低端分数
是GSCE考试的低端分数
00:37
It's the ability to deal with fractions,
percentages and decimals.
percentages and decimals.
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它是运算分数
百分数和小数的能力
百分数和小数的能力
00:40
So this figure prompted
a lot of hand-wringing in Whitehall.
a lot of hand-wringing in Whitehall.
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这个数据使英国行政部门愈发的担忧
00:45
Policies were changed,
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（他们）改变了政策
00:46
investments were made,
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进行了投资
00:48
and then they ran
the survey again in 2011.
the survey again in 2011.
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然后到了2011年
他们再次进行了普查
他们再次进行了普查
00:51
So can you guess
what happened to this number?
what happened to this number?
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你们能猜到这一次的数字吗？
00:56
It went up to 49.
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这次，上升到了49
（观众笑声）
00:57
(Laughter)
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00:58
And in fact, when I reported
this figure in the FT,
this figure in the FT,
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事实上，当我在金融时报
报道这一数据时
报道这一数据时
有个读者开玩笑地说
01:01
one of our readers joked and said,
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“这个数据只震惊了51%的人口”
01:03
"This figure is only shocking
to 51 percent of the population."
to 51 percent of the population."
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（观众笑声）
01:06
(Laughter)
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01:09
But I preferred, actually,
the reaction of a schoolchild
the reaction of a schoolchild
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但我实际上更喜欢一个学生的反应
01:12
when I presented
at a school this information,
at a school this information,
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有一次我在学校展示这一信息时
这个孩子举手说道
01:15
who raised their hand and said,
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01:17
"How do we know that the person
who made that number
who made that number
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“我们怎么知道得出这一数据的人
不是49%中的一个呢？”
01:19
isn't one of the 49 percent either?"
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01:21
(Laughter)
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（观众笑声）
01:22
So clearly, there's a numeracy issue,
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很明显，算术能力成了一个问题
01:26
because these are
important skills for life,
important skills for life,
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毕竟这是生活中的重要技能
更何况我们还想为这个
世纪带来许多的改变
世纪带来许多的改变
01:28
and a lot of the changes
that we want to introduce in this century
that we want to introduce in this century
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01:32
involve us becoming
more comfortable with numbers.
more comfortable with numbers.
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这需要我们对数字越来越应用自如
实际上，不仅英国存在这样的的问题
01:35
Now, it's not just an English problem.
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OECD今年发布了一些
关于青少年计算能力的数据
关于青少年计算能力的数据
01:37
OECD this year released some figures
looking at numeracy in young people,
looking at numeracy in young people,
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01:42
and leading the way, the USA --
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名单里领先的是美国
大约40%的美国青少年
算术能力低下
算术能力低下
01:44
nearly 40 percent of young people
in the US have low numeracy.
in the US have low numeracy.
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01:49
Now, England is there too,
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英国也名列其中
01:50
but there are seven OECD countries
with figures above 20 percent.
with figures above 20 percent.
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但是有七个OECD国家的
数据在20%以上
数据在20%以上
这就有问题了
因为情况本不必如此
因为情况本不必如此
01:56
That is a problem,
because it doesn't have to be that way.
because it doesn't have to be that way.
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在图的最右端
01:59
If you look at the far end of this graph,
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可以看到荷兰，韩国都是个位数
02:01
you can see the Netherlands and Korea
are in single figures.
are in single figures.
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所以，这里绝对有一个算术
问题需要我们解决
问题需要我们解决
02:04
So there's definitely a numeracy
problem that we want to address.
problem that we want to address.
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02:09
Now, as useful as studies like these are,
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和这些研究同样有用的是，
02:12
I think we risk herding people
inadvertently into one of two categories;
inadvertently into one of two categories;
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我认为我们无意间轻率地
将人群分成了两个类别
将人群分成了两个类别
02:17
that there are two kinds of people:
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也就是这么两种人
02:19
those people that are comfortable
with numbers, that can do numbers,
with numbers, that can do numbers,
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对数字可以应用自如
能够把玩数字的人
能够把玩数字的人
02:24
and the people who can't.
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和做不到这一点的人
02:26
And what I'm trying
to talk about here today
to talk about here today
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今天，我想要探讨的是
我认为这是个错误的二分法
02:28
is to say that I believe
that is a false dichotomy.
that is a false dichotomy.
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02:31
It's not an immutable pairing.
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这并不是一成不变的组合
02:33
I think you don't have to have
tremendously high levels of numeracy
tremendously high levels of numeracy
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我认为，你不需要高超的算术能力
02:37
to be inspired by numbers,
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才能被数字所启发
这应该成为前路的起点
02:38
and that should be the starting point
to the journey ahead.
to the journey ahead.
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02:42
And one of the ways in which
we can begin that journey, for me,
we can begin that journey, for me,
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对于我来说，这条路的起始点之一
02:46
is looking at statistics.
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是着眼于统计学
02:48
Now, I am the first to acknowledge
that statistics has got somewhat
that statistics has got somewhat
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这里，我要首先承认
统计是有那么一点儿
统计是有那么一点儿
02:51
of an image problem.
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画面方面的问题的
02:53
(Laughter)
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（观众笑声）
02:54
It's the part of mathematics
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统计是数学的一部分
02:55
that even mathematicians
don't particularly like,
don't particularly like,
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但是它是连数学家
都不怎么喜欢的那一部分
都不怎么喜欢的那一部分
02:59
because whereas the rest of maths
is all about precision and certainty,
is all about precision and certainty,
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因为与其它讲究精确
与确定性的数学领域不同
与确定性的数学领域不同
统计学几乎是反其道而行之
03:03
statistics is almost the reverse of that.
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03:05
But actually, I was a late convert
to the world of statistics myself.
to the world of statistics myself.
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事实上，我自己也是
后来才转向统计学的
后来才转向统计学的
03:10
If you'd asked my undergraduate professors
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如果你们去问问我本科时的教授
03:12
what two subjects would I be least likely
to excel in after university,
to excel in after university,
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我大学毕业后最不可能
有卓越成就的两个领域是什么
有卓越成就的两个领域是什么
03:17
they'd have told you statistics
and computer programming,
and computer programming,
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他们会告诉你是统计和电脑编程
然而现在，我要用我编的程序
03:20
and yet here I am, about to show you
some statistical graphics
some statistical graphics
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给你们展示统计图表
03:23
that I programmed.
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是什么激发我有了这样的转变呢？
03:24
So what inspired that change in me?
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是什么让我对统计产生了兴趣？
03:26
What made me think that statistics
was actually an interesting thing?
was actually an interesting thing?
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03:30
It's really because
statistics are about us.
statistics are about us.
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那是因为统计学是
研究我们自身的科学
研究我们自身的科学
03:32
If you look at the etymology
of the word statistics,
of the word statistics,
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如果你看看统计 (statistics) 的词源
03:35
it's the science of dealing with data
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它是处理数据的科学
03:38
about the state or the community
that we live in.
that we live in.
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围绕着我们所生活的国家或社区
03:40
So statistics are about us as a group,
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所以，统计学是研究
作为一个群体的我们
作为一个群体的我们
03:43
not us as individuals.
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不是作为个人的我们
而且我认为作为一种群居动物
03:45
And I think as social animals,
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我们会分享对作为一个个体，
如何与所在的群体以及同辈之间
如何与所在的群体以及同辈之间
03:47
we share this fascination about how
we as individuals relate to our groups,
we as individuals relate to our groups,
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发生联系的那种痴迷
03:51
to our peers.
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03:52
And statistics in this way
are at their most powerful
are at their most powerful
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统计最强大的时候
03:55
when they surprise us.
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是当它让我们惊讶的时候
03:57
And there's been some really wonderful
surveys carried out recently
surveys carried out recently
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伊普索莫利（英国第二大市场调查组织）
在过去几年进行过
在过去几年进行过
一些很有意义的调查
04:00
by Ipsos MORI in the last few years.
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04:02
They did a survey of over
1,000 adults in the UK,
1,000 adults in the UK,
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他们调查了超过1000个英国的成年人
提出的问题是在每一百个
英格兰和威尔士人当中
英格兰和威尔士人当中
04:05
and said, for every 100 people
in England and Wales,
in England and Wales,
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04:08
how many of them are Muslim?
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有多少个穆斯林？
调查的平均答案
04:10
Now the average answer from this survey,
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是24，这是具有国民代表性的答案
04:13
which was supposed to be representative
of the total population, was 24.
of the total population, was 24.
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04:16
That's what people thought.
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这是国民的想法
英国人认为，在自己国家
每100个人中，有24个信仰穆斯林
每100个人中，有24个信仰穆斯林
04:20
British people think 24 out of every 100
people in the country are Muslim.
people in the country are Muslim.
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而官方数据显示这个数字大约是5
04:24
Now, official figures reveal
that figure to be about five.
that figure to be about five.
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04:29
So there's this big variation
between what we think, our perception,
between what we think, our perception,
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很明显，我们的感知
与被统计资料呈现的事实之间
与被统计资料呈现的事实之间
04:33
and the reality as given by statistics.
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有着巨大的差异
我觉得这很有趣
04:35
And I think that's interesting.
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是什么原因引起
这样的感知差距呢？
这样的感知差距呢？
04:37
What could possibly be causing
that misperception?
that misperception?
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04:41
And I was so thrilled with this study,
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我对这个研究感到很兴奋
并开始在各种演讲中提出这个问题
04:43
I started to take questions out
in presentations. I was referring to it.
in presentations. I was referring to it.
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我曾经在哈默史密斯的
04:46
Now, I did a presentation
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圣保罗女子中学做过一个演讲
04:47
at St. Paul's School for Girls
in Hammersmith,
in Hammersmith,
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当时我有一群像现在这样的观众
04:50
and I had an audience rather like this,
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04:52
except it was comprised entirely
of sixth-form girls.
of sixth-form girls.
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唯一的差别是观众都是
六年级的女学生
六年级的女学生
我说道，“姑娘们，
04:56
And I said, "Girls,
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04:59
how many teenage girls do you think
the British public think
the British public think
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你们觉得英国公众认为
每年有多少个
每年有多少个
青春期女孩怀孕？”
05:04
get pregnant every year?"
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当我说出答案，女孩们是非常生气的
05:05
And the girls were apoplectic when I said
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05:09
the British public think that 15
out of every 100 teenage girls
out of every 100 teenage girls
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我说英国公众认为
每一百个青春期女孩中
每一百个青春期女孩中
每年有15个怀孕
05:13
get pregnant in the year.
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05:15
And they had every right to be angry,
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当然她们有生气的缘由和权利
因为实际上根据官方数字，
05:17
because in fact, I'd have to have
closer to 200 dots
closer to 200 dots
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05:20
before I could color one in,
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（按比例）我必须要有近200个点
05:22
in terms of what
the official figures tell us.
the official figures tell us.
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才能将一个空白圆圈涂满色
05:24
And rather like numeracy,
this is not just an English problem.
this is not just an English problem.
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和计算能力一样
这同样不仅仅是英国的问题
这同样不仅仅是英国的问题
05:28
Ipsos MORI expanded the survey
in recent years to go across the world.
in recent years to go across the world.
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伊普索莫利机构在最近几年
在世界范围内扩展了调查研究
在世界范围内扩展了调查研究
05:32
And so, they asked Saudi Arabians,
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他们问沙特阿拉伯人
05:35
for every 100 adults in your country,
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在你们国家，每一百个成年人中
05:38
how many of them are overweight or obese?
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有多少个超重或是有肥胖症？
05:42
And the average answer from the Saudis
was just over a quarter.
was just over a quarter.
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平均答案是四分之一多一点
05:48
That's what they thought.
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那是他们认为的
仅仅是四分之一多一点的
成年人超重或有肥胖症
成年人超重或有肥胖症
05:49
Just over a quarter of adults
are overweight or obese.
are overweight or obese.
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官方数据显示，实际上
这个数字接近四分之三
这个数字接近四分之三
05:52
The official figures show, actually,
it's nearer to three-quarters.
it's nearer to three-quarters.
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05:57
(Laughter)
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（观众笑声）
这又是一个巨大的差异
05:58
So again, a big variation.
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06:00
And I love this one: they asked in Japan,
they asked the Japanese,
they asked the Japanese,
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下面这个调查我很喜欢
他们问了日本人
他们问了日本人
每一百个日本人里面
06:05
for every 100 Japanese people,
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有多少人住在乡下？
06:07
how many of them live in rural areas?
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06:10
The average was about a 50-50 split,
just over halfway.
just over halfway.
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平均答案大概是一半，
稍高于50%
稍高于50%
06:15
They thought 56 out of every 100
Japanese people lived in rural areas.
Japanese people lived in rural areas.
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他们认为每100个日本人中
有56个居住在乡村地区
有56个居住在乡村地区
06:19
The official figure is seven.
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而官方数字是7
06:22
So extraordinary variations,
and surprising to some,
and surprising to some,
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如此巨大的差异震惊了一些人
06:26
but not surprising to people
who have read the work
who have read the work
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但是对一些人来说并不惊讶
06:29
of Daniel Kahneman, for example,
the Nobel-winning economist.
the Nobel-winning economist.
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比如阅读过诺贝尔经济学奖获得者
丹尼尔·卡内曼的研究成果的人
丹尼尔·卡内曼的研究成果的人
06:33
He and his colleague, Amos Tversky,
spent years researching this disjoint
spent years researching this disjoint
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他和他的同事，阿莫斯·特沃斯基
花费了数年的心血在这个
花费了数年的心血在这个
06:38
between what people perceive
and the reality,
and the reality,
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人们的见解与现实脱节的问题上
实际上人们是直觉很差的统计学家
06:41
the fact that people are actually
pretty poor intuitive statisticians.
pretty poor intuitive statisticians.
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06:45
And there are many reasons for this.
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这背后有着很多原因
个人体验，当然可以影响我们的见解
06:47
Individual experiences, certainly,
can influence our perceptions,
can influence our perceptions,
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但也包括媒体带有偏见
06:50
but so, too, can things like the media
reporting things by exception,
reporting things by exception,
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06:54
rather than what's normal.
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而非全面客观的报道
06:56
Kahneman had a nice way
of referring to that.
of referring to that.
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卡内曼对此有一个很好的描述
他说，“我们对显而易见的
事情视而不见”——
事情视而不见”——
06:59
He said, "We can be blind
to the obvious" --
to the obvious" --
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所以我们有了错误的数字——
07:01
so we've got the numbers wrong --
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“但是我们可以对我们视而不见
这个事实视而不见。”
这个事实视而不见。”
07:02
"but we can be blind
to our blindness about it."
to our blindness about it."
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而这对做决策来说有巨大的影响
07:05
And that has enormous
repercussions for decision making.
repercussions for decision making.
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07:08
So at the statistics office
while this was all going on,
while this was all going on,
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那么在统计学办公室，
当这些事情在发生的时候
当这些事情在发生的时候
我觉得很有趣
07:11
I thought this was really interesting.
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我认为显然这是一个全球性的问题
07:13
I said, this is clearly a global problem,
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但也许与地域差异有关
07:15
but maybe geography is the issue here.
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07:17
These were questions that were all about,
how well do you know your country?
how well do you know your country?
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这里的一些问题都是关于
你对你的国家了解多少
你对你的国家了解多少
在这个问题里，是在问关于
你对6400万人口了解多少？
你对6400万人口了解多少？
07:21
So in this case, it's how well
do you know 64 million people?
do you know 64 million people?
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07:25
Not very well, it turns out.
I can't do that.
I can't do that.
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结果是，我并不是很了解
07:28
So I had an idea,
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所以我有了一个想法
07:29
which was to think about
this same sort of approach
this same sort of approach
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是用相同的方法
但是以一种非常地方化的方式来思考
07:32
but to think about it
in a very local sense.
in a very local sense.
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07:35
Is this a local?
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这是因地制宜的吗？
07:36
If we reframe the questions and say,
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如果我们重新思考问题然后说
你对你的当地状况了解多少？
07:38
how well do you know your local area,
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07:40
would your answers be any more accurate?
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你的答案会更准确吗？
07:43
So I devised a quiz:
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对此我设计了一个测试
07:45
How well do you know your area?
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你对你居住的地区了解多少？
07:48
It's a simple Web app.
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是一个简单的网络应用
07:50
You put in a post code
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输入邮编
它会根据你所在地区的普查数据
07:51
and then it will ask you questions
based on census data
based on census data
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07:54
for your local area.
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给你提出问题
07:56
And I was very conscious
in designing this.
in designing this.
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我在设计时有特别注意
想要让它适用于最广范围的人群
07:58
I wanted to make it open
to the widest possible range of people,
to the widest possible range of people,
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不仅仅是49%的会把玩数字的人
08:02
not just the 49 percent
who can get the numbers.
who can get the numbers.
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08:05
I wanted everyone to engage with it.
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我想要每个人都能参与
所以，针对测试的设计
08:07
So for the design of the quiz,
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我的灵感是由19世纪20到30年代
奥图·纽拉特的
奥图·纽拉特的
08:08
I was inspired by the isotypes
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08:12
of Otto Neurath from the 1920s and '30s.
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“同形像”所激发
08:15
Now, these are methods
for representing numbers
for representing numbers
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这是使用重复图标
08:19
using repeating icons.
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来代表数字的方法
08:21
And the numbers are there,
but they sit in the background.
but they sit in the background.
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那些数字仅仅是作为背景
08:24
So it's a great way
of representing quantity
of representing quantity
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所以这是一个代表数量的绝佳方式
不需要使用“百分比”
08:27
without resorting to using terms
like "percentage,"
like "percentage,"
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08:30
"fractions" and "ratios."
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“分数” 和“比率”
08:31
So here's the quiz.
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这个就是测试界面
08:34
The layout of the quiz is,
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界面的布局是
08:35
you have your repeating icons
on the left-hand side there,
on the left-hand side there,
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你左手边有重复出现的图标
然后这里有一张地图
展现我们问你问题的地区
展现我们问你问题的地区
08:38
and a map showing you the area
we're asking you questions about
we're asking you questions about
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在右手边
08:41
on the right-hand side.
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一共有七个问题
08:43
There are seven questions.
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对于每一个问题，这里有
一个从0到100的可能的答案
一个从0到100的可能的答案
08:44
Each question, there's a possible answer
between zero and a hundred,
between zero and a hundred,
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08:48
and at the end of the quiz,
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在测试结尾
你会得到一个在0和100之间的总分
08:49
you get an overall score
between zero and a hundred.
between zero and a hundred.
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08:52
And so because this is TEDxExeter,
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因为这是TEDx埃克塞特
08:55
I thought we would have
a quick look at the quiz
a quick look at the quiz
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我们可以快速看一下关于
埃克塞特的前几个问题
08:57
for the first few questions of Exeter.
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第一个问题是
08:59
And so the first question is:
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每一百人中
有多少个在十六岁以下？
有多少个在十六岁以下？
09:01
For every 100 people,
how many are aged under 16?
how many are aged under 16?
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09:04
Now, I don't know Exeter very well
at all, so I had a guess at this,
at all, so I had a guess at this,
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当然，我对于埃克塞特不那么了解
所以我随便猜了一下
所以我随便猜了一下
主要是演示这个测试怎样进行
09:08
but it gives you an idea
of how this quiz works.
of how this quiz works.
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09:10
You drag the slider
to highlight your icons,
to highlight your icons,
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你将滑块拖至你所认可的数据图标
09:14
and then just click "Submit" to answer,
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然后点击“递交”
09:16
and we animate away the difference
between your answer and reality.
between your answer and reality.
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然后我们将你的答案和现实之间的
差异用动画的方式表现出来
差异用动画的方式表现出来
09:20
And it turns out, I was a pretty
terrible guess: five.
terrible guess: five.
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结果是，我错得很离谱，
只得了五分
只得了五分
09:25
How about the next question?
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那么下一个问题呢？
这是问关于年龄的中位数
09:26
This is asking about
what the average age is,
what the average age is,
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也就是该地区大于和小于这个年龄的
09:28
so the age at which half
the population are younger
the population are younger
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人口数相等
09:31
and half the population are older.
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我以为是35——
那对我来说是中年
那对我来说是中年
09:32
And I thought 35 -- that sounds
middle-aged to me.
middle-aged to me.
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（观众笑声）
09:36
(Laughter)
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09:40
Actually, in Exeter,
it's incredibly young,
it's incredibly young,
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实际上，在埃克塞特
结果是惊人的年轻
结果是惊人的年轻
09:42
and I had underestimated the impact
of the university in this area.
of the university in this area.
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是我低估了大学在这个区域的影响
随着测试的进行问题的难度会增加
09:46
The questions get harder
as you go through.
as you go through.
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09:48
So this one's now asking
about homeownership:
about homeownership:
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这个问题有关房屋所有权
09:51
For every 100 households, how many
are owned with a mortgage or loan?
are owned with a mortgage or loan?
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在一百个家庭中，有多少个
是有贷款或借债的？
是有贷款或借债的？
我没有直接选出脑海中的数字
09:55
And I hedged my bets here,
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1280
09:56
because I didn't want to be
more than 50 out on the answer.
more than 50 out on the answer.
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因为我不想有超过五十个
10:00
(Laughter)
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（观众笑声）
10:02
And actually, these get harder,
these questions,
these questions,
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实际上，这些问题
变得越来越困难
变得越来越困难
因为当你在一个地区
在一个社区
在一个社区
10:04
because when you're in an area,
when you're in a community,
when you're in a community,
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人口是否老龄化或是年轻化
这样的事情，是很容易找到线索的
这样的事情，是很容易找到线索的
10:07
things like age -- there are clues
to whether a population is old or young.
to whether a population is old or young.
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看看周围环境就知道了
10:12
Just by looking around
the area, you can see it.
the area, you can see it.
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像房屋所有权这样的事
要观察起来就困难得多
要观察起来就困难得多
10:15
Something like homeownership
is much more difficult to see,
is much more difficult to see,
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所以我们还原了我们的启发法
10:18
so we revert to our own heuristics,
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关于我们自认为有
多少人拥有房屋所有权的偏差
多少人拥有房屋所有权的偏差
10:21
our own biases about how many people
we think own their own homes.
we think own their own homes.
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10:25
Now the truth is,
when we published this quiz,
when we published this quiz,
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然而真相是
当我们发表这个测试时
当我们发表这个测试时
10:29
the census data that it's based on
was already a few years old.
was already a few years old.
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我们所依据的普查数据
已经是好几年前的了
已经是好几年前的了
10:32
We've had online applications
that allow you to put in a post code
that allow you to put in a post code
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我们有一个在线应用
你可以输入邮编
你可以输入邮编
然后得到过去几年的数据
10:36
and get statistics back for years.
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10:38
So in some senses,
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所以，在某种程度上
这是有点儿过时的数据
10:39
this was all a little bit old
and not necessarily new.
and not necessarily new.
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10:43
But I was interested to see
what reaction we might get
what reaction we might get
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但是我很感兴趣
会得到什么样的反应
会得到什么样的反应
用我们已有的方式去使数据游戏化
10:47
by game-ifying the data
in the way that we have,
in the way that we have,
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10:49
by using animation
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通过使用动画
以及利用人们会有先入之见这一事实
10:51
and playing on the fact
that people have their own preconceptions.
that people have their own preconceptions.
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10:55
It turns out, the reaction was, um ...
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结果是，反应...
11:00
was more than I could have hoped for.
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比我所期望的要好
11:02
It was a long-held ambition of mine
to bring down a statistics website
to bring down a statistics website
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对我来说运营数据网站
需要持久的雄心
需要持久的雄心
因为公众有着庞大的需求
11:05
due to public demand.
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11:07
(Laughter)
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（观众笑声）
这个网址包含单词“统计“，"gov" 和"UK"
11:08
This URL contains the words
"statistics," "gov" and "UK,"
"statistics," "gov" and "UK,"
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这三个是在网址中最不受欢迎的单词
11:12
which are three of people's least
favorite words in a URL.
favorite words in a URL.
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11:15
And the amazing thing about this
was that the website came down
was that the website came down
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惊人的是这个网站在晚上
11:19
at quarter to 10 at night,
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9点45的时候瘫痪了
11:21
because people were actually
engaging with this data
engaging with this data
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因为人们实际上在基于自愿的基础上
11:25
of their own free will,
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很积极地利用他们的
个人时间为我们提供数据
11:26
using their own personal time.
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11:28
I was very interested to see
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我很兴奋的看到
有二十五万人在测试发起的
11:31
that we got something like
a quarter of a million people
a quarter of a million people
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48小时之内参与了测试
11:34
playing the quiz within the space
of 48 hours of launching it.
of 48 hours of launching it.
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11:38
And it sparked an enormous discussion
online, on social media,
online, on social media,
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并且在网络和社交媒体上
激发了巨大的讨论
激发了巨大的讨论
这些讨论大部分被
11:42
which was largely dominated
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在见解偏差中找到乐趣的人们所占据
11:44
by people having fun
with their misconceptions,
with their misconceptions,
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3993
11:48
which is something that
I couldn't have hoped for any better,
I couldn't have hoped for any better,
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对于我来说，在某种程度上没有比这
11:51
in some respects.
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更令人欣慰的了
我也愿意看到人们开始将它发送给政客
11:52
I also liked the fact that people started
sending it to politicians.
sending it to politicians.
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你对你自己声称代表的区域了解多少？
11:55
How well do you know the area
you claim to represent?
you claim to represent?
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（观众笑声）
11:58
(Laughter)
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那么我的演讲要接近尾声了
11:59
And then just to finish,
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12:01
going back to the two kinds of people,
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回到那两类人
12:04
I thought it would be
really interesting to see
really interesting to see
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我很好奇
擅长数字的人是如何完成这个测试的
12:06
how people who are good with numbers
would do on this quiz.
would do on this quiz.
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12:09
The national statistician
of England and Wales, John Pullinger,
of England and Wales, John Pullinger,
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英格兰和威尔士的国家统计学家，
约翰·普令吉尔
约翰·普令吉尔
你会觉得他应该做得非常好
12:12
you would expect he would be pretty good.
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12:15
He got 44 for his own area.
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然而他在自己的区域得了44分
12:17
(Laughter)
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（观众笑声）
12:20
Jeremy Paxman -- admittedly,
after a glass of wine -- 36.
after a glass of wine -- 36.
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杰瑞米·拍客斯曼——我得声明
是在喝了一杯酒之后——得了36分
是在喝了一杯酒之后——得了36分
12:26
Even worse.
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还不如前面那位
12:27
It just shows you that the numbers
can inspire us all.
can inspire us all.
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它展示给大家数字可以启示我们
12:30
They can surprise us all.
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可以给我们带来惊讶
12:32
So very often, we talk about statistics
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所以我们常常把统计学
作为一种不确定性的科学来谈论
12:34
as being the science of uncertainty.
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1962
12:36
My parting thought for today is:
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我今天的想法是
实际上，统计是关于我们自身的科学
12:37
actually, statistics is the science of us.
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12:40
And that's why we should
be fascinated by numbers.
be fascinated by numbers.
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这是为什么我们应该为此感到着迷
12:43
Thank you very much.
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非常感谢
12:44
(Applause)
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（观众掌声）
ABOUT THE SPEAKER
Alan Smith - Data visualisation editorAlan Smith uses interactive graphics and statistics to breathe new life into how data is presented.
Why you should listen
Alan Smith is Data Visualisation Editor at the Financial Times in London. Previously he was Head of Digital Content at the UK Office for National Statistics (ONS).
With a background in cartography and digital mapping, he has spent the last decade finding ways of bringing statistics to wider audiences. In 2010, he was an inaugural recipient of the Royal Statistical Society's Award for Excellence in Official Statistics. He was appointed Office of the Order of the British Empire (OBE) in the Queen's 2011 Birthday Honours list.
Alan Smith | Speaker | TED.com