Name: 何謂標準？我們都不一樣，我們也都很正常 --（What is “normal” and what is “different”? - Yana Buhrer Tavanier）
Uploaded: 2022-04-17T21:01:30.000Z
Duration: 5 min 8 s

["If you are always trying to be normal, you will never know how amazing you can be." - Maya Angelou]

[「如果你總是試著想變得普通，你將永遠無法得知自己能變得多棒。」 - Maya Angelou]

In 1945, two sculptures meant to represent the average man and woman in the United States went on exhibit at the American Museum of Natural History.

在 1945 年時，兩座本來代表著美國平均男性與女性的雕像在美國自然歷史博物館中展出。

基礎被動語態

Based on measurements taken from tens of thousands of young men and women, they were called Norma and Normman.

基於對數萬名年輕男子與女子的身體測量所製作，它們被稱為諾瑪與諾曼。

That same year, a contest launched to find a living embodiment of Norma.

在同一年，官方舉辦了一場找出誰才是「活生生的諾瑪」。

Normal is often used as a synonym for "typical," "expected," or even "correct."

「標準」這個詞常被用來當作「典型的」、「預料中的」，或甚至是「正確的」的同義詞。

By that logic, most people should fit  the description of normal.

從這個邏輯來看，大部分的人應該都與「標準」相符。

And yet, not one of almost 4,000 women  who participated in the contest matched Norma, the supposedly "normal" woman.

然而，參賽的 4000 名女性卻沒有一個與諾瑪，也就是最「標準」的女性形象完全相同。

This puzzle isn't unique  to Norma and Normman, either—time and time again,  so-called normal descriptions of our bodies, minds, and perceptions  have turned out to match almost no one.

這樣令人費解的狀況並不只有在諾瑪和諾曼身上展現－一而再再而三的我們發現，根本沒有人真的完全符合我們對於身體、心靈與認知的「標準」形象。

And yet, a lot of the world is constructed around a foundation of normalcy.

然而世界上很大一部分仍以這樣的標準概念為基礎所建構。

So what does normal actually mean—and should we be relying on it so much?

所以標準到底是什麼意思？我們真的該如此仰賴這個概念嗎？

In statistics, a normal distribution  describes  a set of values that fall along a bell curve.

從統計學上來說，常態分佈的意思是數值沿著一個鐘形的曲線分布。

The average, or mean, of all the values is at the very center, and most other values fall  within the hump of the bell.

所有數值的平均值位於中心，而其他大多數的數值則位於鐘狀區域內部。

These curves can be tall, with most values inside a narrow range, or long and flat, with only a slight bias towards the average.

曲線本身可能很陡峭，代表多數的值都落在一個狹窄的範圍；或也可能又長又扁，只有少數數值位於平均值附近。

What makes the distribution normal  is that it follows this curved shape.

只要分布的曲線符合這樣的鐘形，就算是標準分布。

Normal doesn't describe a single data  point, but a pattern of diversity.

這裡的標準指的不是單一個資料點，而是多樣性的分布模式。

Many human traits, like height, follow a normal distribution.

像是身高等的人類特質也具有常態分布的特徵。

Some people are very tall or very short, but most people fall close  to the overall average.

有些人非常高或非常矮，而大多數人則更接近整體的平均值。

Outside of statistics, normal often refers to an average—like the single number pulled  from the fattest part of the bell curve—that eliminates all the nuance of the normal distribution.

在統計學外，標準通常指的是平均值，像是從鐘形曲線中最寬的部分提取出單一個數字那樣，而這樣的說法卻把常態分布的細微差異給抹煞了。

Norma and Norman's proportions came from such averages.

諾瑪與諾曼的身形就來自這種平均值。

Applied to individuals,  whether someone is considered normal usually depends on how closely  they hew to this average.

而套用到一個人身上時，一個人是否標準取決於他們與這種平均值之間的距離。

At best, such definitions of normal  fail to capture variation.

最多，這種對標準的定義是無法描繪出多樣性。

But oftentimes, our calculations of normal are even more flawed.

但大部分的時候，我們對與標準的計算則更加失準。

BMI is a measure of weight relative to height, with different ratios  falling into "underweight," "normal weight," "overweight," and "obese" ranges.

BMI 是一個能計算出體重與身高相對關係的系統，並將最後的比例劃分為「過輕」、「標準體重」、「過重」與「肥胖」等範圍。

Generally, only BMIs that correspond to normal weight are considered healthy.

一般來說，只有與符合標準體重範圍的 BMI 數值才算是健康體型。

But BMI is not always  an accurate predictor of health, or even of what's a healthy weight.

然而 BMI 並非對於健康狀況，或甚至是對於何為健康體重的準確指標。

BMI doesn't take into account  body fat percentage, body fat distribution, levels of physical activity, or blood pressure.

BMI 並沒有將體脂肪比率、體脂肪分布、體適能或血壓考量進去。

And yet, those who fall outside the so-called normal range are commonly advised that losing or gaining weight will improve their health.

然而那些數值在標準範圍內的人，常會被建議要減少或增加體重才能改善健康。

When we apply a standard of normal to all of humanity that's based on data  from a non-representative slice,

當我們將一個基於非代表性人群的資料所推斷出的標準標準，加諸於全部人身上時，

we're not just choosing one point on the distribution, we're choosing it  from the wrong distribution.

我們不但只挑出來了分布上的一個點，而且還是從錯誤的分布上挑選出來的。

A lot of behavior science research draws from samples that are pretty WEIRD—meaning Western, educated,  industrialized, rich, and democratic.

許多行為科學研究都是從 WEIRD (怪異) 人群中抽樣的：西方 (W)、受過教育 (E)、工業化 (I)、富有 (R) 且民主 (D)。

These features can skew norms even in research that doesn't have an obvious link to them.

這些特質可能會扭曲了標準的概念，就連在看似與它們沒有明顯關聯的研究中也一樣。

Take the famed  Muller-Lyer optical illusion: it's normal to think one  of the two lines is longer, when they're actually the same length.

以著名的繆莱二氏錯覺為例：通常會認為兩條線鐘其中一條比較常，但其實兩條線一樣長。

At least, it is if you're an American undergraduate.

至少，如果你是名美國大學生的話。

A team of anthropologists  and psychologists found other demographic groups were much less susceptible—members of the San people of the Kalahari weren't susceptible  to the illusion at all.

一個由人類學家與心理學家所組成的團隊發現其他的人口群體更不容易受到這個錯覺現象的影響－來自喀拉哈里的桑族人對這個錯覺完全免疫。

When these limited or inaccurate definitions of normal are used to make decisions  that impact people's lives, they can do real harm.

當這些具有侷限或是不準確的「標準」定義被用來做為可能會影響到人們性命的決定的依據時，便會有顯著危害。

Historically, such concepts of normal have been hugely influential.

歷史上來說，這種「標準」的概念曾經大為盛行。

The Eugenics Movement of the early 20th  century weaponized the concept of normal, using it to justify exclusion, violence,  and even extermination of those deemed not normal.

二十世紀早期的優生學運動將這樣對於普通的概念武裝化，將它作為正當化暴力、分化，甚至是屠殺被認為非普通人的理由。

To this day, people are often targeted and discriminated against on the basis of disabilities,  mental health issues, sexual orientations, gender identities, and other features deemed "not normal."

直到今日，人們仍會因為殘疾、心理健康問題、性取向、性別身分等其他被認為「不標準」的特質而被針對與歧視。

But the reality is that the differences in our bodies, minds, perceptions, and ideas about the world around us—in short, diversity— is the true normal.

但實際上，我們在身體、心靈、認知與世界觀的不同－也就是我們多元性－才是再普通不過的事情。

And speaking of deeply flawed system of measurement, how much do you know about the dark history of IQ tests? Learn more with this video.

說到充滿缺陷的測量系統，你知道 IQ 測驗的黑暗過往嗎？觀看這部影片來了解更多吧。

Or get the latest on an important debate: Should we get rid of standardized testing?

或是跟上最近一個相當重要的辯論議題：我們該不該廢除標準化考試？