What we miss when we focus on the average | Am I Normal? with Mona Chalabi

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2021-11-02 ・ TED


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What we miss when we focus on the average | Am I Normal? with Mona Chalabi

95,638 views ・ 2021-11-02

TED


μ•„λž˜ μ˜λ¬Έμžλ§‰μ„ λ”λΈ”ν΄λ¦­ν•˜μ‹œλ©΄ μ˜μƒμ΄ μž¬μƒλ©λ‹ˆλ‹€.

00:00
Transcriber:
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When we think about data, we usually think about averages.
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λ²ˆμ—­: Jin Choi κ²€ν† : DK Kim
데이터에 λŒ€ν•΄ 생각할 λ•Œ μš°λ¦¬λŠ” 보톡 평균을 λ– μ˜¬λ¦½λ‹ˆλ‹€.
00:03
Average height, average salary,
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평균 ν‚€, 평균 연봉,
00:05
average number of hours spent on video calls.
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μ˜μƒ 톡화에 μ“°λŠ” 평균 톡화 μ‹œκ°„.
00:07
It’s tempting to focus on these neat little summaries of our world.
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세상에 λŒ€ν•œ 간단λͺ…λ£Œν•œ μš”μ•½μ— μ£Όλͺ©ν•˜κ³  μ‹Άμ–΄μ§‘λ‹ˆλ‹€.
00:11
But the world is a lot messier than these averages can make it out to be.
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κ·ΈλŸ¬λ‚˜ 세상은 평균듀보닀 훨씬 더 λ³΅μž‘ν•©λ‹ˆλ‹€.
00:14
So instead, I look for the outliers.
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κ·Έλž˜μ„œ λŒ€μ‹ μ— μ €λŠ” 아웃라이어λ₯Ό μ°Ύμ•„λ΄…λ‹ˆλ‹€.
00:17
They can offer a better reflection of this chaos we call life.
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μ•„μ›ƒλΌμ΄μ–΄λŠ” 삢이라고 λΆ€λ₯΄λŠ” 이 ν˜Όλˆμ„ 더 잘 λ°˜μ˜ν•  수 μžˆμŠ΅λ‹ˆλ‹€.
00:20
And they can offer a different perspective
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그리고 μ΄ν•΄ν•œλ‹€κ³  μƒκ°ν•˜λŠ” 것듀에 λŒ€ν•΄ 또 λ‹€λ₯Έ 관점을 쀄 수 μžˆμŠ΅λ‹ˆλ‹€.
00:23
on the things that we think we understand.
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00:25
[Am I Normal? with Mona Chalabi]
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[μ œκ°€ μ •μƒμΈκ°€μš”? - λͺ¨λ‚˜ 찰라비]
00:27
Take, for instance, the stats around teens and cigarettes.
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μ‹­ λŒ€μ™€ 담배에 κ΄€ν•œ 톡계λ₯Ό 예둜 λ“€μ–΄ λ³Όκ²Œμš”.
00:30
According to the CDC, between 1997 and 2019,
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λ―Έκ΅­ μ§ˆλ³‘ν†΅μ œμ˜ˆλ°©μ„Όν„°μ— λ”°λ₯΄λ©΄ 1997λ…„κ³Ό 2019λ…„ 사이에
00:34
the percentage of American high school students who smoked plummeted
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λ―Έκ΅­ κ³ λ“±ν•™μƒμ˜ 흑연λ₯ μ΄
36%μ—μ„œ 단 6%둜 κΈ‰κ°ν–ˆμŠ΅λ‹ˆλ‹€.
00:37
from 36 to just six percent.
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00:39
That seems like a pretty big win,
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κ½€ 쒋은 μ„±κ³Όμ²˜λŸΌ λ³΄μ΄μ§€λ§Œ
00:41
but when you break apart the data and look at the outliers,
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자료λ₯Ό μͺΌκ°œμ–΄ 아웃라이어λ₯Ό μ‚΄νŽ΄λ³΄λ©΄ μ™„μ „νžˆ λ‹€λ₯Έ 상황이 λ©λ‹ˆλ‹€.
00:44
it is a totally different picture.
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λ―Έκ΅­ 원주민과 μ•Œλž˜μŠ€μΉ΄ μΆœμ‹  ν•™μƒλ“€μ—μ„œ
00:46
Among American Indian and native Alaskan students,
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00:48
cigarette usage is much higher than that six percent average.
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흑연λ₯ μ΄ ν‰κ· μΉ˜μΈ 6%보닀 훨씬 λ†’μŠ΅λ‹ˆλ‹€.
00:51
It comes in at a sizable 21 percent.
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μƒλ‹Ήνžˆ 높은 수치인 21%λ‚˜ λ©λ‹ˆλ‹€.
00:54
All other racial and ethnic groups were in the single digits.
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λ‹€λ₯Έ λͺ¨λ“  인쒅과 λ―Όμ‘± μ§‘λ‹¨μ˜ 흑연λ₯ μ€ ν•œ μžλ¦¬μ˜€μŠ΅λ‹ˆλ‹€.
00:57
So what first seemed like this great success story
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κ·ΈλŸ¬λ‹ˆκΉŒ μ²˜μŒμ— μœ„λŒ€ν•œ μ„±κ³΅λ‹΄μ²˜λŸΌ λ³΄μ˜€λ˜ 것은
01:00
is actually an indicator of how much work we need to do
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사싀은 κ°€μž₯ μ†Œμ™Έλœ 곡동체에 λ„λ‹¬ν•˜κΈ° μœ„ν•΄
01:03
to reach some of the most marginalized communities.
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μš°λ¦¬κ°€ μ–Όλ§ˆλ‚˜ 더 주의λ₯Ό κΈ°μšΈμ—¬μ•Ό ν•˜λŠ”κ°€μ— λŒ€ν•œ μ§€ν‘œμž…λ‹ˆλ‹€.
01:06
In general, when we present data as a scatterplot,
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일반적으둜 자료λ₯Ό 점으둜 μ°μ–΄μ„œ 보면
01:09
the average would usually look like this.
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평균은 보톡 μ΄λ ‡κ²Œ μƒκ²ΌμŠ΅λ‹ˆλ‹€.
01:11
And where there are outliers,
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아웃라이어가 μžˆμ„ λ•Œ 일반적인 접근법은 μ•„μ›ƒλΌμ΄μ–΄μ˜ κ°€μΉ˜λ₯Ό ν‰κ°€μ ˆν•˜ν•˜κ³ 
01:12
the typical approach is to undervalue them,
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01:15
to see them as a deviation from the average
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ν‰κ· μ—μ„œ λ˜λŠ” μ‚¬νšŒκ°€ μƒκ°ν•˜λŠ” μ •μƒμ—μ„œ μΌνƒˆν•œ κ²ƒμœΌλ‘œ λ΄…λ‹ˆλ‹€.
01:17
or from what society thinks is normal.
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01:19
But I like to call these outliers β€œlost birds.”
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ν•˜μ§€λ§Œ μ €λŠ” 이 아웃라이어듀을 β€˜κΈΈ μžƒμ€ μƒˆλ“€β€™μ΄λΌ λΆ€λ₯΄κ³  μ‹Άμ–΄μš”.
01:23
It's a nickname I use for something or someone who has gone astray.
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이건 μ œκ°€ λ°©ν™©ν•˜κ³  μžˆλŠ” μ‚¬λžŒμ΄λ‚˜ 무언가λ₯Ό λΆ€λ₯Ό λ•Œ μ“°λŠ” λ³„μΉ­μ΄μ—μš”.
01:27
If you look hard enough,
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꼼꼼히 λ“€μ—¬λ‹€ λ³Έλ‹€λ©΄
01:28
you'll find that these lost birds pop up everywhere.
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이 κΈΈ μžƒμ€ μƒˆλ“€μ΄ λͺ¨λ“  κ³³μ—μ„œ νŠ€μ–΄λ‚˜μ˜¨λ‹€λŠ” κ±Έ μ•Œκ²Œ 될 κ±°μ˜ˆμš”.
01:32
Like my mom, for example.
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예λ₯Ό λ“€μ–΄ 저희 μ—„λ§ˆμ²˜λŸΌμš”.
01:34
She doesn't like being on camera, so this puppet will have to do.
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μ—„λ§ˆλŠ” 사진 μ°νžˆλŠ” κ±Έ μ‹«μ–΄ν•˜λ‹ˆ μΈν˜•μ΄ λŒ€μ‹ ν•  κ±°μ˜ˆμš”.
01:37
She's a soft spoken, hijabi woman who isn't much bigger than this puppet.
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μ—„λ§ˆλŠ” 이 μΈν˜•λ³΄λ‹€ 많이 크지 μ•Šκ³ , νžˆμž‘μ„ μ“΄ 상λƒ₯ν•œ μ—¬μ„±μž…λ‹ˆλ‹€.
01:40
Because of that, it's easy for some people to underestimate her.
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κ·Έλž˜μ„œ μ–΄λ–€ μ‚¬λžŒλ“€μ€ μ—„λ§ˆλ₯Ό μ‰½κ²Œ κ³Όμ†Œν‰κ°€ν–ˆμ–΄μš”.
01:43
But don't let those first impressions fool you.
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ν•˜μ§€λ§Œ 첫인상에 속지 λ§ˆμ„Έμš”.
β€œλ‚΄ μ„ΈλŒ€μ—μ„œλŠ”
01:46
β€œIn my generation,
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01:47
we used to listen and accept what they tell us.
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λ‹€λ₯Έ μ‚¬λžŒλ“€μ˜ 말을 λ“£κ³  μˆ˜μš©ν•˜λŠ” 것에 μ΅μˆ™ν–ˆμ–΄.
01:51
'Do what you're told.'
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β€˜μ‹œν‚€λŠ” λŒ€λ‘œ 해라.’
01:53
But when I got older,
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ν•˜μ§€λ§Œ λ‚΄κ°€ λ‚˜μ΄κ°€ λ“€κ³  λ‚˜μ„œ
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I just changed and I started to argue my point and get what I want."
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λ‚˜λŠ” λ°”λ€Œμ—ˆκ³  λ‚΄ μ˜κ²¬μ„ μ£Όμž₯ν–ˆκ³  μ›ν•˜λŠ” κ±Έ μ–»μ–΄λƒˆμ§€.”
02:01
My mom's a retired doctor, an avid ugly-dress maker,
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저희 μ—„λ§ˆλŠ” μ€ν‡΄ν•œ μ˜μ‚¬μ΄κ³ , λͺ»μƒκΈ΄ μ˜·μ„ μ—΄μ‹¬νžˆ λ§Œλ“œμ‹œκ³ 
02:04
a mother of two and a grandmother of none.
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두 μ•„μ΄μ˜ μ—„λ§ˆμ΄μž 손주 μ—†λŠ” ν• λ¨Έλ‹ˆμ˜ˆμš”.
02:06
Though she spends a fair amount of time trying to speak that into existence,
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손주λ₯Ό λ‚³μœΌλΌκ³  μ„€λ“ν•˜λŠ” 데 κ½€ λ§Žμ€ μ‹œκ°„μ„ μ“°κΈ΄ ν•˜μ…¨μ§€λ§Œμš”.
02:10
"I think for every mother, for her daughter, she wants a grandchild."
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β€œλ‚΄ 생각에 λͺ¨λ“  μ—„λ§ˆλ“€μ€ 자기 λ”Έμ—κ²Œ 손주λ₯Ό 원해.”
02:15
(Laughter)
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(μ›ƒμŒ)
02:18
"Sorry, Mona."
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β€œλ―Έμ•ˆ, λͺ¨λ‚˜.”
02:19
Moving on.
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λ„˜μ–΄κ°€μ£ .
02:20
My mom is also a lost bird.
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저희 μ—„λ§ˆ λ˜ν•œ κΈΈ μžƒμ€ μƒˆμž…λ‹ˆλ‹€.
02:22
"Me?"
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β€œλ‚˜?”
02:23
She has, statistically speaking, gone astray.
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ν†΅κ³„ν•™μ μœΌλ‘œ λ§ν•˜λ©΄, μ—„λ§ˆλŠ” μ›λž˜μ˜ 길을 λ²—μ–΄λ‚¬μ–΄μš”.
02:26
"Yeah, but it was a good deviation."
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β€œκ·Έλž˜, ν•˜μ§€λ§Œ 쒋은 μΌνƒˆμ΄μ—ˆμ–΄.”
02:28
Back in the late '70s,
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70λ…„λŒ€ ν›„λ°˜μ— μ—„λ§ˆλŠ” μ˜ν•™ μˆ˜μ—…μ„ λ°›κ³  κ°œμ—…μ„ ν•˜λ €κ³ 
02:29
my mom left Iraq and moved to the UK
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02:31
to further her medical training and practice.
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이라크λ₯Ό λ– λ‚˜ 영ꡭ으둜 μ΄μ£Όν–ˆμŠ΅λ‹ˆλ‹€.
02:33
She's among the four percent of people born in Iraq who now live abroad.
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μ—„λ§ˆλŠ” μ΄λΌν¬μ—μ„œ νƒœμ–΄λ‚˜ μ™Έκ΅­μ—μ„œ μ‚΄κ³  μžˆλŠ” 4% μ•ˆμ— λ“  μ‚¬λžŒμž…λ‹ˆλ‹€.
02:37
By the early 2000s,
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2000λ…„λŒ€ μ΄ˆλ°˜μ—λŠ”
02:38
just three percent of UK doctors with her experience
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μ—„λ§ˆμ˜ 경우λ₯Ό ν¬ν•¨ν•΄μ„œ 영ꡭ μ˜μ‚¬μ˜ 였직 3%만이
02:41
were non-white and practicing in her speciality.
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μœ μƒ‰μΈμ’…μ΄μ—ˆκ³  μ—„λ§ˆμ™€ 같은 λΆ„μ•Όμ—μ„œ κ°œμ—…ν•˜κ³  μžˆμ—ˆμŠ΅λ‹ˆλ‹€.
02:44
My mom is a lost bird because she is an outlier.
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저희 μ—„λ§ˆλŠ” κΈΈ μžƒμ€ μƒˆμΈλ° μ™œλƒν•˜λ©΄ μ•„μ›ƒλΌμ΄μ–΄μ΄κ±°λ“ μš”.
02:47
She's one of the rare few to leave her home country
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μ—„λ§ˆλŠ” 쑰ꡭ을 λ– λ‚œ 정말 μ†Œμˆ˜μ˜ μ‚¬λžŒλ“€ 쀑 ν•œ λͺ…이고
02:49
and even rarer still among her medical peers.
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μ—„λ§ˆμ™€ 같은 의료인 μ€‘μ—μ„œλŠ” μ—¬μ „νžˆ 더 μ†Œμˆ˜μ— μ†ν•©λ‹ˆλ‹€.
02:52
We all think that the people that we love are special,
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μš°λ¦¬λŠ” λͺ¨λ‘ μ‚¬λž‘ν•˜λŠ” μ‚¬λžŒλ“€μ„ νŠΉλ³„ν•˜λ‹€κ³  μƒκ°ν•˜κ³ 
02:54
and there is some truth to that.
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그건 μ–΄λŠ 정도 μ‚¬μ‹€μž…λ‹ˆλ‹€.
02:56
But it’s worth considering the ways that we are all lost birds.
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κ·ΈλŸ¬λ‚˜ 우리 λͺ¨λ‘λŠ” κΈΈ μžƒμ€ μƒˆλΌκ³  λ³΄λŠ” 방식을 κ³ λ €ν•  κ°€μΉ˜κ°€ μžˆμŠ΅λ‹ˆλ‹€.
μ™œλƒν•˜λ©΄ 평균에 μ£Όλͺ©ν•˜κ³  아웃라이어듀을 λ¬΄μ‹œν•  λ•Œ
02:59
Because when we focus on the average and we ignore the outliers,
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03:02
we lose all of the richness and insights that those stories provide.
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κ·Έλ“€μ˜ 이야기가 μ£ΌλŠ” 톡찰λ ₯κ³Ό ν’μš”λ‘œμ›€ λͺ¨λ‘λ₯Ό μžƒκΈ° λ•Œλ¬Έμž…λ‹ˆλ‹€.
03:05
But when we dig into the deviations, we get to see the bigger picture.
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κ·Έ μΌνƒˆμ„ νŒŒκ³ λ“€μ–΄ 보면 더 큰 그림을 λ³Ό 수 μžˆμŠ΅λ‹ˆλ‹€.
03:09
One from a bird's-eye view.
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높이 λ‚˜λŠ” μƒˆμ˜ 눈으둜 λ³Έ κ·Έλ¦Όμ„μš”.
이 μ›Ήμ‚¬μ΄νŠΈ 정보

이 μ‚¬μ΄νŠΈλŠ” μ˜μ–΄ ν•™μŠ΅μ— μœ μš©ν•œ YouTube λ™μ˜μƒμ„ μ†Œκ°œν•©λ‹ˆλ‹€. μ „ 세계 졜고의 μ„ μƒλ‹˜λ“€μ΄ κ°€λ₯΄μΉ˜λŠ” μ˜μ–΄ μˆ˜μ—…μ„ 보게 될 κ²ƒμž…λ‹ˆλ‹€. 각 λ™μ˜μƒ νŽ˜μ΄μ§€μ— ν‘œμ‹œλ˜λŠ” μ˜μ–΄ μžλ§‰μ„ 더블 ν΄λ¦­ν•˜λ©΄ κ·Έκ³³μ—μ„œ λ™μ˜μƒμ΄ μž¬μƒλ©λ‹ˆλ‹€. λΉ„λ””μ˜€ μž¬μƒμ— 맞좰 μžλ§‰μ΄ μŠ€ν¬λ‘€λ©λ‹ˆλ‹€. μ˜κ²¬μ΄λ‚˜ μš”μ²­μ΄ μžˆλŠ” 경우 이 문의 양식을 μ‚¬μš©ν•˜μ—¬ λ¬Έμ˜ν•˜μ‹­μ‹œμ˜€.

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