TEDNYC
Mona Chalabi: 3 ways to spot a bad statistic
夢娜．夏拉比: 三個辨識劣質統計數據的方法
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有時候真的很難判斷哪些統計數據是值得信任的。我們不能全然相信統計數據，而是應該要學會看懂數據背後的意義。在這場愉快又充滿幽默的演說中，數據記者夢娜．夏拉比與我們分享她的幾個拿手妙招，可以幫助我們對數據進行質疑、解讀，並真正地理解數據背後的意義。
Mona Chalabi - Data journalist
Mona Chalabi tries to take the numb out of numbers. She's left with lots of "ers." Full bio
Mona Chalabi tries to take the numb out of numbers. She's left with lots of "ers." Full bio
Double-click the English transcript below to play the video.
00:12
Now, I'm going to be talking
about statistics today.
about statistics today.
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今天我要來談談統計。
00:15
If that makes you immediately feel
a little bit wary, that's OK,
a little bit wary, that's OK,
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如果讓你感覺到
一點點的焦慮，沒關係，
一點點的焦慮，沒關係，
這場演講不會讓你變成
瘋狂的陰謀論者，
瘋狂的陰謀論者，
00:18
that doesn't make you some
kind of crazy conspiracy theorist,
kind of crazy conspiracy theorist,
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00:21
it makes you skeptical.
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它能讓你學會懷疑。
00:23
And when it comes to numbers,
especially now, you should be skeptical.
especially now, you should be skeptical.
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一提到數據，特別是現在，
你更要懷疑。
你更要懷疑。
但你也必須要有能力
判讀哪些數據是可靠的，
判讀哪些數據是可靠的，
00:26
But you should also be able to tell
which numbers are reliable
which numbers are reliable
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哪些是不可靠的。
00:29
and which ones aren't.
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00:31
So today I want to try to give you
some tools to be able to do that.
some tools to be able to do that.
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所以我今天要教大家
一些判斷的工具。
一些判斷的工具。
但在這之前，
00:34
But before I do,
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00:35
I just want to clarify which numbers
I'm talking about here.
I'm talking about here.
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我想要先說明
我所談論的是哪一種數據。
我所談論的是哪一種數據。
我並不是要談類似這樣的數據：
00:38
I'm not talking about claims like,
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00:40
"9 out of 10 women recommend
this anti-aging cream."
this anti-aging cream."
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「十位女性當中有九位
會推薦這款抗老化乳液」
會推薦這款抗老化乳液」
我們很多人聽到那樣的說法
會不相信而翻眼珠。
會不相信而翻眼珠。
00:42
I think a lot of us always
roll our eyes at numbers like that.
roll our eyes at numbers like that.
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但是我現在要談的，
是人們會質疑的一些統計數據，
是人們會質疑的一些統計數據，
00:45
What's different now is people
are questioning statistics like,
are questioning statistics like,
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例如「美國的失業率是 5% 」。
00:48
"The US unemployment
rate is five percent."
rate is five percent."
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兩者的差異在於後者這宣稱
（失業率）並非來自私人企業，
（失業率）並非來自私人企業，
00:50
What makes this claim different is
it doesn't come from a private company,
it doesn't come from a private company,
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而是來自政府機構。
00:54
it comes from the government.
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實際上，如今每十個美國人當中
00:55
About 4 out of 10 Americans
distrust the economic data
distrust the economic data
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就有四個人根本不相信
政府公布的經濟數據。
政府公布的經濟數據。
00:58
that gets reported by government.
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01:00
Among supporters of President Trump
it's even higher;
it's even higher;
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而川普總統的支持者當中，
不相信的比例更高，
不相信的比例更高，
01:03
it's about 7 out of 10.
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大約十個人裡面會有七個。
01:04
I don't need to tell anyone here
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我並不想在這裡解釋
在目前社會中的許多分界線；
01:06
that there are a lot of dividing lines
in our society right now,
in our society right now,
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一旦你了解政府公佈的數據
與民眾之間的關係，
與民眾之間的關係，
01:09
and a lot of them start to make sense,
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01:11
once you understand people's relationships
with these government numbers.
with these government numbers.
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這些分界線就開始變得有意義了。
一方面，有些人認為
這些數據是至關重要的，
這些數據是至關重要的，
01:15
On the one hand, there are those who say
these statistics are crucial,
these statistics are crucial,
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01:18
that we need them to make sense
of society as a whole
of society as a whole
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這些數據能讓我們
瞭解整個社會的狀況，
瞭解整個社會的狀況，
為了就是要避免
各種情感上的糾葛，
各種情感上的糾葛，
01:21
in order to move beyond
emotional anecdotes
emotional anecdotes
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01:23
and measure progress in a subjective way.
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並且以客觀的方式
衡量政策的發展。
衡量政策的發展。
另外一群人則認為，
01:25
And then there are the others,
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01:27
who say that these statistics are elitist,
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這些統計數據
都是來自菁英份子，
都是來自菁英份子，
甚至可能是受到操縱的；
01:29
maybe even rigged;
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這些數據沒有意義，
而且根本無法真正反映
而且根本無法真正反映
01:30
they don't make sense
and they don't really reflect
and they don't really reflect
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一般民眾的日常生活狀況。
01:33
what's happening
in people's everyday lives.
in people's everyday lives.
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01:35
It kind of feels like that second group
is winning the argument right now.
is winning the argument right now.
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目前看來，主張第二種觀點的人
似乎是對的。
似乎是對的。
01:38
We're living in a world
of alternative facts,
of alternative facts,
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我們生活的世界中
胡說八道已成常態，
胡說八道已成常態，
01:41
where people don't find statistics
this kind of common ground,
this kind of common ground,
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民眾對這些數據沒有基本共識，
也不會把這些數據
視為爭論時的基準點。
視為爭論時的基準點。
01:44
this starting point for debate.
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這會是個問題。
01:45
This is a problem.
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01:46
There are actually
moves in the US right now
moves in the US right now
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實際上，目前有一股風潮
正在席捲美國，
正在席捲美國，
01:49
to get rid of some government
statistics altogether.
statistics altogether.
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他們認為應該要全面擺脫
政府統計數據的束縛。
政府統計數據的束縛。
01:51
Right now there's a bill in congress
about measuring racial inequality.
about measuring racial inequality.
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目前國會正在審查一項有關
評估種族不平等的法案。
評估種族不平等的法案。
01:55
The draft law says that government
money should not be used
money should not be used
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草案中主張，
政府不應該把經費運用於
政府不應該把經費運用於
01:58
to collect data on racial segregation.
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收集各種有關種族隔離的資料上。
02:00
This is a total disaster.
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這簡直是一場災難。
如果我們缺乏這樣的資料，
02:02
If we don't have this data,
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我們要如何觀察種族歧視現象？
02:03
how can we observe discrimination,
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更不用提要如何修正它？
02:05
let alone fix it?
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換句話說：
02:06
In other words:
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如果政府無法衡量
目前不公的程度，
目前不公的程度，
02:08
How can a government create fair policies
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02:10
if they can't measure
current levels of unfairness?
current levels of unfairness?
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他們要如何制訂公平的政策？
這也不只是攸關歧視的問題，
02:12
This isn't just about discrimination,
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02:14
it's everything -- think about it.
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也會牽扯到所有的事情，各位想想：
02:16
How can we legislate on health care
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如果我們沒有
健康或貧困的正確數據，
健康或貧困的正確數據，
02:18
if we don't have good data
on health or poverty?
on health or poverty?
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我們要如何制訂
衛生保健的相關法案？
衛生保健的相關法案？
如果我們連有多少人正要移入、
遷出我們的國家，
遷出我們的國家，
02:20
How can we have public debate
about immigration
about immigration
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都缺乏一致的共識，
02:22
if we can't at least agree
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我們要如何對於移民政策
進行公開的辯論？
進行公開的辯論？
02:24
on how many people are entering
and leaving the country?
and leaving the country?
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統計（Statistics） 這個字，
就是源自於國家事務（State）。
就是源自於國家事務（State）。
02:26
Statistics come from the state;
that's where they got their name.
that's where they got their name.
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重點是，要更精確地
測量人口的分布，
測量人口的分布，
02:29
The point was to better
measure the population
measure the population
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才能為社會大眾提供更好的服務。
02:31
in order to better serve it.
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所以我們需要政府的數據，
02:33
So we need these government numbers,
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但我們也需要摒除全盤接受
02:35
but we also have to move
beyond either blindly accepting
beyond either blindly accepting
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或是全盤否定的迷思。
02:37
or blindly rejecting them.
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02:39
We need to learn the skills
to be able to spot bad statistics.
to be able to spot bad statistics.
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我們需要學會
辨識劣質統計數據的方法。
辨識劣質統計數據的方法。
02:42
I started to learn some of these
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當我在聯合國的統計部門工作時，
02:43
when I was working
in a statistical department
in a statistical department
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我開始學會了一些辨識的技巧。
02:45
that's part of the United Nations.
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我們的工作是要了解
有多少伊拉克人民
有多少伊拉克人民
02:47
Our job was to find out how many Iraqis
had been forced from their homes
had been forced from their homes
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因為戰爭而被迫離開家鄉，
02:50
as a result of the war,
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並且了解他們的需求。
02:52
and what they needed.
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02:53
It was really important work,
but it was also incredibly difficult.
but it was also incredibly difficult.
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這是很重要的工作，
但也非常困難。
但也非常困難。
02:56
Every single day, we were making decisions
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我們每天所作的決策，
02:58
that affected the accuracy
of our numbers --
of our numbers --
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都會影響數據的準確性，
03:00
decisions like which parts
of the country we should go to,
of the country we should go to,
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像是我們應該要前往
這個國家的哪些地區、
這個國家的哪些地區、
我們要與誰談話、
03:03
who we should speak to,
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03:04
which questions we should ask.
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應該問哪些問題...等等。
03:06
And I started to feel
really disillusioned with our work,
really disillusioned with our work,
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但我對於工作的幻想
很快就破滅了，
很快就破滅了，
因為我們自認這項工作很有意義，
03:08
because we thought we were doing
a really good job,
a really good job,
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但是能夠告訴我們
真實情況的伊拉克民眾，
真實情況的伊拉克民眾，
03:11
but the one group of people
who could really tell us were the Iraqis,
who could really tell us were the Iraqis,
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他們根本沒機會看到我們的分析，
更別說是提出質疑了。
更別說是提出質疑了。
03:14
and they rarely got the chance to find
our analysis, let alone question it.
our analysis, let alone question it.
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03:18
So I started to feel really determined
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所以我愈來愈確信，
03:20
that the one way to make
numbers more accurate
numbers more accurate
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要讓數據更為準確的方法，
03:22
is to have as many people as possible
be able to question them.
be able to question them.
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就是盡量讓更多人對數據提出質疑。
03:25
So I became a data journalist.
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所以我變成一位數據記者。
03:27
My job is finding these data sets
and sharing them with the public.
and sharing them with the public.
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我的工作就是找到這些資料，
並且公開分享給社會大眾。
並且公開分享給社會大眾。
03:30
Anyone can do this,
you don't have to be a geek or a nerd.
you don't have to be a geek or a nerd.
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任何人都能做得到，
你不需要是個技術極客或是怪咖。
你不需要是個技術極客或是怪咖。
你不用理會這些名詞；
03:34
You can ignore those words;
they're used by people
they're used by people
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這是某些人想要表現聰明，
卻假裝謙虛時所用的字眼。
卻假裝謙虛時所用的字眼。
03:36
trying to say they're smart
while pretending they're humble.
while pretending they're humble.
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任何人絕對都可以做到。
03:39
Absolutely anyone can do this.
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所以我想給各位三個問題，
03:41
I want to give you guys three questions
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它們可以幫助你辨識出
劣質的統計數據。
劣質的統計數據。
03:43
that will help you be able to spot
some bad statistics.
some bad statistics.
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03:46
So, question number one
is: Can you see uncertainty?
is: Can you see uncertainty?
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問題一：
你是否能看出數據的不確定性？
你是否能看出數據的不確定性？
03:49
One of things that's really changed
people's relationship with numbers,
people's relationship with numbers,
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有件事真正會改變
民眾與數據的關係，
民眾與數據的關係，
甚至改變對媒體的信任，
03:53
and even their trust in the media,
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其中一個方式就是
對選舉民調的濫用。
對選舉民調的濫用。
03:54
has been the use of political polls.
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我個人對選舉民調的
報導方式很有意見，
報導方式很有意見，
03:57
I personally have a lot of issues
with political polls
with political polls
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因為我認為記者扮演的角色，
就只是報導事實，
就只是報導事實，
03:59
because I think the role of journalists
is actually to report the facts
is actually to report the facts
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而不是嘗試著預測結果，
04:02
and not attempt to predict them,
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特別是那些會傷害民主
的選舉預測，
的選舉預測，
04:04
especially when those predictions
can actually damage democracy
can actually damage democracy
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像是暗示選民說：
別再費心給那個傢伙投票了，
別再費心給那個傢伙投票了，
04:07
by signaling to people:
don't bother to vote for that guy,
don't bother to vote for that guy,
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他根本沒機會當選。
04:10
he doesn't have a chance.
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我們把這個話題擺一邊，
先來談談這樣做的效果如何。
先來談談這樣做的效果如何。
04:11
Let's set that aside for now and talk
about the accuracy of this endeavor.
about the accuracy of this endeavor.
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根據幾個國家的選舉，
像是英國、義大利、以色列，
像是英國、義大利、以色列，
04:15
Based on national elections
in the UK, Italy, Israel
in the UK, Italy, Israel
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當然還有最近的美國總統大選，
04:19
and of course, the most recent
US presidential election,
US presidential election,
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可以看到運用民調來預測選舉結果，
04:22
using polls to predict electoral outcomes
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04:24
is about as accurate as using the moon
to predict hospital admissions.
to predict hospital admissions.
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準確度就像觀測天象來預測
是否應該住院，同樣的不可靠。
是否應該住院，同樣的不可靠。
04:28
No, seriously, I used actual data
from an academic study to draw this.
from an academic study to draw this.
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說真的，我用了一份學術研究報告
的真實資料，畫出這張圖。
的真實資料，畫出這張圖。
04:32
There are a lot of reasons why
polling has become so inaccurate.
polling has become so inaccurate.
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民調變得不準確，有很多原因。
我們的社會已經變得相當多元化，
04:36
Our societies have become really diverse,
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讓從事民意調查的人很難挑選出
04:38
which makes it difficult for pollsters
to get a really nice representative sample
to get a really nice representative sample
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真正能代表選民意願的樣本。
04:42
of the population for their polls.
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人們已經很厭倦回答民調電話，
04:44
People are really reluctant to answer
their phones to pollsters,
their phones to pollsters,
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而且令人震驚的是，
受訪者還可能會說謊。
受訪者還可能會說謊。
04:47
and also, shockingly enough,
people might lie.
people might lie.
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04:49
But you wouldn't necessarily
know that to look at the media.
know that to look at the media.
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但是你在媒體報導中
不會知道這些事情。
不會知道這些事情。
04:52
For one thing, the probability
of a Hillary Clinton win
of a Hillary Clinton win
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例如希拉蕊·柯林頓
贏得選舉的機率，
贏得選舉的機率，
04:55
was communicated with decimal places.
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竟然可以精確到小數點？
我們描述氣溫都不會這麽精確。
04:57
We don't use decimal places
to describe the temperature.
to describe the temperature.
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所以怎麼可能對於全國
二億三千萬選民的行為，
二億三千萬選民的行為，
05:00
How on earth can predicting the behavior
of 230 million voters in this country
of 230 million voters in this country
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能夠做出如此精確的預測？
05:04
be that precise?
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05:06
And then there were those sleek charts.
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還有一些看似井然有條的圖表，
各位知道嗎，有許多的視覺化設計，
05:08
See, a lot of data visualizations
will overstate certainty, and it works --
will overstate certainty, and it works --
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會誇大資料的準確性，而且很有效。
05:12
these charts can numb
our brains to criticism.
our brains to criticism.
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這些圖表會麻痺我們的大腦，
讓我們無法做出判斷。
讓我們無法做出判斷。
05:15
When you hear a statistic,
you might feel skeptical.
you might feel skeptical.
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當你聽到一個統計數據，
你可能會覺得懷疑。
你可能會覺得懷疑。
05:17
As soon as it's buried in a chart,
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但是當數據變成了圖表，
05:19
it feels like some kind
of objective science,
of objective science,
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看起來就成為客觀的科學調查結果，
但實際上並非如此。
05:21
and it's not.
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05:22
So I was trying to find ways
to better communicate this to people,
to better communicate this to people,
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所以，我試著找出一些方法，
清楚地告訴大家這些事，
清楚地告訴大家這些事，
05:26
to show people the uncertainty
in our numbers.
in our numbers.
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讓大家知道數據本身的不確定性。
而我所做的，就是把這些數據
05:28
What I did was I started taking
real data sets,
real data sets,
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用手繪的視覺化設計來呈現，
05:30
and turning them into
hand-drawn visualizations,
hand-drawn visualizations,
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05:33
so that people can see
how imprecise the data is;
how imprecise the data is;
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好讓人們可以看到
資料是如此的不精確；
資料是如此的不精確；
05:36
so people can see that a human did this,
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所以大家會看到，
有人作了這個調查，
有人作了這個調查，
05:38
a human found the data and visualized it.
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然後有人找到這些數據，
並且將它視覺化。
並且將它視覺化。
05:40
For example, instead
of finding out the probability
of finding out the probability
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舉個例子，
我們不去找出每個月
民眾患流行性感冒的機率，
民眾患流行性感冒的機率，
05:42
of getting the flu in any given month,
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而是得到整個流感季節
的大致分布情形。
的大致分布情形。
05:45
you can see the rough
distribution of flu season.
distribution of flu season.
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就是這一張圖。
05:47
This is --
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05:49
(Laughter)
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（笑聲）
正值二月，這數據真不適時宜。
05:50
a bad shot to show in February.
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但這樣的視覺化呈現方式
是比較可靠的，
是比較可靠的，
05:51
But it's also more responsible
data visualization,
data visualization,
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05:54
because if you were to show
the exact probabilities,
the exact probabilities,
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因為如果你是用精確的機率來呈現，
也許會誤導民眾
05:56
maybe that would encourage
people to get their flu jabs
people to get their flu jabs
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在錯誤的時間注射疫苗。
05:59
at the wrong time.
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重點是這些歪七扭八的線條，
06:01
The point of these shaky lines
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能讓人們記得「數據的不精確性」，
06:02
is so that people remember
these imprecisions,
these imprecisions,
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人們不應該滿足於
一個鷄肋的數字，
一個鷄肋的數字，
06:05
but also so they don't necessarily
walk away with a specific number,
walk away with a specific number,
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而是要能夠記得重要的事實。
06:09
but they can remember important facts.
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有些不正義和不公平的事實，
在我們生活中造成了巨大的影響。
在我們生活中造成了巨大的影響。
06:10
Facts like injustice and inequality
leave a huge mark on our lives.
leave a huge mark on our lives.
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像是美國黑人及原住民的預期壽命
06:15
Facts like Black Americans and Native
Americans have shorter life expectancies
Americans have shorter life expectancies
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比其他族群來的短，
06:19
than those of other races,
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1400
06:20
and that isn't changing anytime soon.
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而且這是短時間內難以改變的事實。
06:22
Facts like prisoners in the US
can be kept in solitary confinement cells
can be kept in solitary confinement cells
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還有像是美國監獄中，
囚犯的個人牢房空間
囚犯的個人牢房空間
06:26
that are smaller than the size
of an average parking space.
of an average parking space.
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比一般停車位的平均面積
還要小的事實。
還要小的事實。
06:30
The point of these visualizations
is also to remind people
is also to remind people
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這些視覺化圖像的重點
就是為了要提醒大家，
就是為了要提醒大家，
關注一些真正重要的統計概念，
06:33
of some really important
statistical concepts,
statistical concepts,
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像是關於「平均數」的概念。
06:36
concepts like averages.
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例如你聽到有人說：
06:37
So let's say you hear a claim like,
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「在美國，每座游泳池裡面
平均有 6.23 次大便」。
平均有 6.23 次大便」。
06:39
"The average swimming pool in the US
contains 6.23 fecal accidents."
contains 6.23 fecal accidents."
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06:44
That doesn't mean every single
swimming pool in the country
swimming pool in the country
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它的意思不是說，每一座游泳池
06:46
contains exactly 6.23 turds.
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都有剛剛好 6.23 次大便。
06:49
So in order to show that,
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為了說明這件事，
06:50
I went back to the original data,
which comes from the CDC,
which comes from the CDC,
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我找到疾病管制局的原始資料，
06:53
who surveyed 47 swimming facilities.
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他們總共調查了47 座游泳池。
06:55
And I just spent one evening
redistributing poop.
redistributing poop.
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我花了一個晚上「重新分配大便」。
06:57
So you can kind of see
how misleading averages can be.
how misleading averages can be.
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所以你就可以看出，
平均數如何地誤導大家。
平均數如何地誤導大家。
07:00
(Laughter)
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（笑聲）
07:01
OK, so the second question
that you guys should be asking yourselves
that you guys should be asking yourselves
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好，第二個辨識
劣質統計數據的方法，
劣質統計數據的方法，
07:05
to spot bad numbers is:
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就是你要問自己：
07:07
Can I see myself in the data?
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我自己的情況體現在這份數據內嗎？
這個問題也與平均數有關，
07:09
This question is also
about averages in a way,
about averages in a way,
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07:12
because part of the reason
why people are so frustrated
why people are so frustrated
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因為民眾會對於國家的統計數據
產生失望的一部份原因，
07:14
with these national statistics,
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07:16
is they don't really tell the story
of who's winning and who's losing
of who's winning and who's losing
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是因為在國家的政策中，
他們無法完全地看出
誰是贏家、誰是輸家。
誰是贏家、誰是輸家。
07:19
from national policy.
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很容易理解，
為什麼當全球的平均數字
為什麼當全球的平均數字
07:20
It's easy to understand why people
are frustrated with global averages
are frustrated with global averages
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與民眾的個人經驗不一致時，
他們會感到失望不已。
他們會感到失望不已。
07:24
when they don't match up
with their personal experiences.
with their personal experiences.
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我想告訴人們與我們
日常生活相關的數據。
日常生活相關的數據。
07:26
I wanted to show people the way
data relates to their everyday lives.
data relates to their everyday lives.
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07:30
I started this advice column
called "Dear Mona,"
called "Dear Mona,"
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我開設了一個專欄《親愛的夢娜》，
人們會寫信詢問一些
他們所關心的事情，
他們所關心的事情，
07:32
where people would write to me
with questions and concerns
with questions and concerns
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我會試著用數據回答他們。
07:35
and I'd try to answer them with data.
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人們會問我任何事情，
07:37
People asked me anything.
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像是「跟老婆分床睡是正常的嗎？」
07:38
questions like, "Is it normal to sleep
in a separate bed to my wife?"
in a separate bed to my wife?"
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「人們會對身上的刺青覺得後悔嗎？」
07:41
"Do people regret their tattoos?"
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「自然死亡」是甚麼意思？
07:43
"What does it mean to die
of natural causes?"
of natural causes?"
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07:45
All of these questions are great,
because they make you think
because they make you think
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所有的問題都很棒，
因為這些問題會讓你思考，
因為這些問題會讓你思考，
用什麼方法尋找並傳達這些數字。
07:48
about ways to find
and communicate these numbers.
and communicate these numbers.
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如果有人問你，「尿多少尿才算太多？」
07:50
If someone asks you,
"How much pee is a lot of pee?"
"How much pee is a lot of pee?"
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07:53
which is a question that I got asked,
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我真的曾經被問過這個問題，
07:55
you really want to make sure
that the visualization makes sense
that the visualization makes sense
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你會很想用視覺化圖像來表達，
這樣可以盡量讓更多人理解。
07:58
to as many people as possible.
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08:00
These numbers aren't unavailable.
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這些數字不是找不到。
08:02
Sometimes they're just buried
in the appendix of an academic study.
in the appendix of an academic study.
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有時候，數據只是被埋沒在
學術研究的附錄裡。
學術研究的附錄裡。
08:05
And they're certainly not inscrutable;
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但是它們並非難以理解的；
08:07
if you really wanted to test
these numbers on urination volume,
these numbers on urination volume,
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如果你真的想要檢驗
這些有關尿量的數據，
這些有關尿量的數據，
你自己拿個瓶子試試就知道了。
08:10
you could grab a bottle
and try it for yourself.
and try it for yourself.
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（笑聲）
08:12
(Laughter)
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08:13
The point of this isn't necessarily
197
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重點是，這些數據
並不是每樣都要與你有關。
08:15
that every single data set
has to relate specifically to you.
has to relate specifically to you.
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我對於「法國有多少女人
因為戴面紗與頭巾而被罰款」
因為戴面紗與頭巾而被罰款」
08:18
I'm interested in how many women
were issued fines in France
were issued fines in France
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這樣的議題很感興趣，
08:21
for wearing the face veil, or the niqab,
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即使我不住法國也不戴面紗。
08:23
even if I don't live in France
or wear the face veil.
or wear the face veil.
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08:26
The point of asking where you fit in
is to get as much context as possible.
is to get as much context as possible.
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問自己是否符合數據當中的情況，
是為了儘量得到更多的事件脈絡。
是為了儘量得到更多的事件脈絡。
08:29
So it's about zooming out
from one data point,
from one data point,
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所以我們要更宏觀地觀察數據，
08:32
like the unemployment rate
is five percent,
is five percent,
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像是失業率 5% 這類的數據，
可以觀察它如何隨著時間而變化，
08:34
and seeing how it changes over time,
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08:35
or seeing how it changes
by educational status --
by educational status --
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或看看它在不同教育程度的差異──
08:38
this is why your parents always
wanted you to go to college --
wanted you to go to college --
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這也許是爸媽希望你進大學的原因──
08:41
or seeing how it varies by gender.
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或是看它在不同性別上的表現。
08:43
Nowadays, male unemployment rate is higher
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如今，男性的失業率
已經比女性高了。
08:45
than the female unemployment rate.
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08:47
Up until the early '80s,
it was the other way around.
it was the other way around.
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但是在 80 年代初期之前，
情況是相反的。
情況是相反的。
這是美國社會到目前為止，
08:50
This is a story of one
of the biggest changes
of the biggest changes
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其中一項最大的改變，
08:52
that's happened in American society,
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一旦你眼光放遠，不被平均數字侷限，
這些訊息都存在圖表當中。
這些訊息都存在圖表當中。
08:54
and it's all there in that chart,
once you look beyond the averages.
once you look beyond the averages.
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軸線能呈現數據的各種意義；
08:57
The axes are everything;
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08:58
once you change the scale,
you can change the story.
you can change the story.
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當你改變觀察的尺度，
你就能得到新的結論。
你就能得到新的結論。
09:01
OK, so the third and final question
that I want you guys to think about
that I want you guys to think about
217
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好，第三個也是最後一個問題，
當你觀察統計數據時
我希望各位去思考的是：
我希望各位去思考的是：
09:04
when you're looking at statistics is:
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這些數據是如何收集而來的？
09:06
How was the data collected?
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09:09
So far, I've only talked about the way
data is communicated,
data is communicated,
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目前為止，我只談論到
呈現數據的方式，
呈現數據的方式，
但收集資料的方式也同樣重要。
09:12
but the way it's collected
matters just as much.
matters just as much.
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我知道這很困難，
09:14
I know this is tough,
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因為收集數據的方法，
經常是不透明而且有些無聊的，
經常是不透明而且有些無聊的，
09:16
because methodologies can be opaque
and actually kind of boring,
and actually kind of boring,
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但有一些步驟
可以給各位用來檢視數據。
可以給各位用來檢視數據。
09:19
but there are some simple steps
you can take to check this.
you can take to check this.
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這裡我要舉最後一個例子。
09:22
I'll use one last example here.
225
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1839
09:24
One poll found that 41 percent of Muslims
in this country support jihad,
in this country support jihad,
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一份民調指出，國內有 41% 的
穆斯林支持伊斯蘭聖戰，
穆斯林支持伊斯蘭聖戰，
09:28
which is obviously pretty scary,
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1525
聽起來相當嚇人，
09:29
and it was reported everywhere in 2015.
228
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2642
這份調查在 2015 年被大肆報導。
09:32
When I want to check a number like that,
229
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2615
當我想檢驗這樣的數據時，
我會先尋找原始的問卷。
09:35
I'll start off by finding
the original questionnaire.
the original questionnaire.
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09:37
It turns out that journalists
who reported on that statistic
who reported on that statistic
231
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2926
結果發現，報導這則新聞的記者，
忽略了問卷當中的一個問題，
09:40
ignored a question
lower down on the survey
lower down on the survey
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題目中詢問了受訪者
「如何定義伊斯蘭聖戰？」
「如何定義伊斯蘭聖戰？」
09:42
that asked respondents
how they defined "jihad."
how they defined "jihad."
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2346
大多數人的定義是：
09:45
And most of them defined it as,
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1981
「為了更虔誠的信仰，穆斯林所進行
個人的、和平的內心鬥爭」。
個人的、和平的內心鬥爭」。
09:47
"Muslims' personal, peaceful struggle
to be more religious."
to be more religious."
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只有 16% 的人認為是
「對抗不信教者的暴力神聖戰爭」。
「對抗不信教者的暴力神聖戰爭」。
09:51
Only 16 percent defined it as,
"violent holy war against unbelievers."
"violent holy war against unbelievers."
236
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所以真正的重點是：
09:55
This is the really important point:
237
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根據原本的數據，很有可能
09:57
based on those numbers,
it's totally possible
it's totally possible
238
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2155
那些將聖戰
定義為暴力神聖戰爭的人，
定義為暴力神聖戰爭的人，
09:59
that no one in the survey
who defined it as violent holy war
who defined it as violent holy war
239
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3105
根本不支持聖戰。
10:03
also said they support it.
240
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1332
10:04
Those two groups might not overlap at all.
241
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2208
這兩群人可能沒有根本重疊。
10:07
It's also worth asking
how the survey was carried out.
how the survey was carried out.
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問卷調查的進行方式
也值得我們探討。
也值得我們探討。
這次的民調是一種稱為
「自願參與」的調查方式，
「自願參與」的調查方式，
10:09
This was something called an opt-in poll,
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意思就是，任何人都可以上網
找到並且參與這項調查。
找到並且參與這項調查。
10:11
which means anyone could have found it
on the internet and completed it.
on the internet and completed it.
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你沒有辦法得知參與者
是否真的是穆斯林。
是否真的是穆斯林。
10:15
There's no way of knowing
if those people even identified as Muslim.
if those people even identified as Muslim.
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而且最後只有 600 個人
參與了那份民調。
參與了那份民調。
10:18
And finally, there were 600
respondents in that poll.
respondents in that poll.
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根據皮尤研究中心的資料，
10:21
There are roughly three million
Muslims in this country,
Muslims in this country,
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我們國內大約有三百萬名
伊斯蘭教信徒。
伊斯蘭教信徒。
10:23
according to Pew Research Center.
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意思就是國內每五千名穆斯林當中，
10:25
That means the poll spoke to roughly
one in every 5,000 Muslims
one in every 5,000 Muslims
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大約只有一位填寫了那份問卷。
10:28
in this country.
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這也是為什麼政府的統計數據，
10:29
This is one of the reasons
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10:31
why government statistics
are often better than private statistics.
are often better than private statistics.
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通常比私人機構的調查
更為準確的原因之一。
更為準確的原因之一。
一項民調可能訪談了幾百或一千人，
10:34
A poll might speak to a couple
hundred people, maybe a thousand,
hundred people, maybe a thousand,
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或者以萊雅公司在 2005 年
嘗試銷售護膚產品為例，
嘗試銷售護膚產品為例，
10:37
or if you're L'Oreal, trying to sell
skin care products in 2005,
skin care products in 2005,
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只訪談了 48 位
認為產品有效的女性就好了。
認為產品有效的女性就好了。
10:40
then you spoke to 48 women
to claim that they work.
to claim that they work.
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（笑聲）
10:43
(Laughter)
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私人公司沒多少興趣
去追求數據的正確性，
去追求數據的正確性，
10:44
Private companies don't have a huge
interest in getting the numbers right,
interest in getting the numbers right,
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他們只需要「對」的數字。
10:47
they just need the right numbers.
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但是政府的統計人員可不能如此。
10:49
Government statisticians aren't like that.
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至少在理論上，他們必須完全公正，
10:51
In theory, at least,
they're totally impartial,
they're totally impartial,
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特別是因為他們大多數都很盡職，
不受掌權者所影響。
不受掌權者所影響。
10:54
not least because most of them do
their jobs regardless of who's in power.
their jobs regardless of who's in power.
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他們都是人民的公僕。
10:57
They're civil servants.
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而為了做好份內的事，
10:58
And to do their jobs properly,
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他們不能只調查幾百人。
11:00
they don't just speak
to a couple hundred people.
to a couple hundred people.
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我所引用的失業率數字
11:03
Those unemployment numbers
I keep on referencing
I keep on referencing
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來自美國勞動統計局，
11:05
come from the Bureau of Labor Statistics,
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為了這項估計，
11:07
and to make their estimates,
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他們調查超過 14 萬家國內企業。
11:08
they speak to over 140,000
businesses in this country.
businesses in this country.
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11:12
I get it, it's frustrating.
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我懂，聽到這些很令人沮喪。
11:14
If you want to test a statistic
that comes from a private company,
that comes from a private company,
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如果你想檢驗私人企業的
統計數據是否正確，
統計數據是否正確，
你可以替自己或其他朋友
買面霜來試用，
買面霜來試用，
11:17
you can buy the face cream for you
and a bunch of friends, test it out,
and a bunch of friends, test it out,
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如果覺得沒有效果，
你就可以說他們的數據有誤。
你就可以說他們的數據有誤。
11:20
if it doesn't work,
you can say the numbers were wrong.
you can say the numbers were wrong.
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但是你要如何
對政府的統計數據提出質疑呢？
對政府的統計數據提出質疑呢？
11:23
But how do you question
government statistics?
government statistics?
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你需要檢查這些數據的方方面面。
11:25
You just keep checking everything.
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找出他們是如何收集這些數據的。
11:27
Find out how they collected the numbers.
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找出圖表中是否有你需要的全部訊息。
11:29
Find out if you're seeing everything
on the chart you need to see.
on the chart you need to see.
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但是也不要完全放棄數據，
因為如果你放棄了，
因為如果你放棄了，
11:32
But don't give up on the numbers
altogether, because if you do,
altogether, because if you do,
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我們就會受私人利益的誤導，
11:35
we'll be making public policy
decisions in the dark,
decisions in the dark,
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在無知的狀態下，
制訂出錯誤的公共政策。
制訂出錯誤的公共政策。
11:37
using nothing but private
interests to guide us.
interests to guide us.
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謝謝各位。
11:40
Thank you.
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（掌聲）
11:41
(Applause)
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ABOUT THE SPEAKER
Mona Chalabi - Data journalistMona Chalabi tries to take the numb out of numbers. She's left with lots of "ers."
Why you should listen
After working for a humanitarian organisation, Mona Chalabi saw how important data was, but also how easily it could be used by people with their own specific agendas. Since then, her work for organizations like Transparency International and The Guardian has had one goal: to make sure as many people as possible can find and question the data they need to make informed decisions about their lives.
Chalabi is currently the Data Editor of the Guardian US, where she writes articles, produces documentaries and turns data into illustrations and animations. In 2016, her data illustrations were commended by the Royal Statistical Society.
Mona Chalabi | Speaker | TED.com