What is “normal” and what is “different”? - Yana Buhrer Tavanier

翻译人员: Siyu Lian 校对人员: Thomas Tam

In 1945, two sculptures meant to represent the average man and woman

1945 年，两座旨在代表 美国普通男性容貌和女性的雕塑，

in the United States

went on exhibit at the American Museum of Natural History.

在美国自然历史博物馆展出。

Based on measurements taken from tens of thousands of young men and women,

两座雕塑的容貌基于数以万计 青年男女的测量数据，

它们被命名为“诺玛”和“诺曼”。

they were called Norma and Normman.

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

人们在同年发起一场比赛， 试图找到“诺玛”的真人原型。

Normal is often used as a synonym for “typical,” “expected,”

“正常”通常被用作 “典型的” 和“意料之中的”，

or even “correct.”

甚至是“正确的”的同义词。

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

以这种逻辑来看，

大部分人都应该符合正常的描述。

And yet, not one of almost 4,000 women who participated in the contest

然而，在将近 4,000 名 参加比赛的女性里，

matched Norma, the supposedly “normal” woman.

没有一个人和诺玛，人们想象中 “正常”的女性相符。

This puzzle isn’t unique to Norma and Normman, either—

这个令人费解的现象

不仅出现在诺玛和诺曼中。

time and time again, so-called normal descriptions

不止一次那些对我们身体、

of our bodies, minds, and perceptions have turned out to match almost no one.

思想和感受的所谓“正常描述“ 却几乎不符合任何人。

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

然而，大部分的世界都是 围绕着正常的基础而构建的。

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

正常实际上意味着什么？ 我们应该如此依赖这个概念吗？

In statistics, a normal distribution describes a set of values

从统计学看一个正常的 正态分布图描述的是

that fall along a bell curve.

落在钟形曲线上的一组数值。

The average, or mean, of all the values is at the very center,

在最中心的是所有数值的平均值，

and most other values fall within the hump of the bell.

大部分的其他数值 都落在钟形的驼峰内。

These curves can be tall, with most values inside a narrow range,

如果曲线高耸，则大部分数值集中 在一个很窄的范围内。

or long and flat, with only a slight bias towards the average.

曲线低平则说明 数值和平均值间仅有微小差距。

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

决定数据分布正常与否的是 其是否遵循这个弯曲的曲线形态。

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

正常描述的不是单一的一点 而是一组多元的数据。

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

人类的许多特质，例如身高 就符合正态分布。

Some people are very tall or very short,

有的人很高，有的人很矮，

but most people fall close to the overall average.

但大部分人都很接近总平均值。

Outside of statistics, normal often refers to an average—

在统计学以外， 正常一般指的是平均值。

like the single number pulled from the fattest part of the bell curve—

如从钟形曲线最宽厚部分 抽出的单一数字一样。

that eliminates all the nuance of the normal distribution.

它消除了正态分布 中的所有细微差别。

Norma and Norman’s proportions came from such averages.

诺玛和诺曼的比例正是 来自于这样的平均值。

Applied to individuals, whether someone is considered normal

对于个体来说， 一个人被认为是否正常

usually depends on how closely they hew to this average.

通常取决于他们 与平均值的接近程度。

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

该种对正常的定义无法捕捉变量。

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

然而通常情况下，

我们对于正常的估算 甚至存在更大的缺陷。

Take the BMI— or Body Mass Index.

以BMI（体质指数）为例，

BMI is a measure of weight relative to height,

BMI是一种相对于 身高的体重测量。

with different ratios falling into “underweight,”

不同的比例决定着 一个人是在“过瘦”、

“normal weight,” “overweight,” and “obese” ranges.

“正常体重” 、“超重” 或是“肥胖”的范围内。

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

通常来说，

只有BMI指数符合正常体重 才会被视为健康。

But BMI is not always an accurate predictor of health,

但BMI指数并不总能 准确地预测健康，

or even of what’s a healthy weight.

甚至什么是一个健康的体重。

BMI doesn’t take into account body fat percentage,

BMI没有考虑到体脂率、 体脂肪分布、

body fat distribution, levels of physical activity, or blood pressure.

运动的频率或者血压。

And yet, those who fall outside the so-called normal range

然而，却普遍建议 在正常数值以外的人们

are commonly advised that losing or gaining weight

减重或是增重 去提高他们的健康水平。

will improve their health.

When we apply a standard of normal to all of humanity

当我们用一个正常的标准 来衡量全人类，

that’s based on data from a non-representative slice,

而衡量正常的标准来自于 不具代表性的切片中的数据。

we’re not just choosing one point on the distribution,

我们不只是在分布上 选择了某个值,

we’re choosing it from the wrong distribution.

我们更是在错误的分布上 选择了它。

A lot of behavior science research draws from samples that are pretty WEIRD—

许多的行为科学研究 使用的样本都十分“奇怪”，

meaning Western, educated, industrialized, rich, and democratic.

意思是这些样本都是来自西方的、

受到良好教育的、工业化的、 富有的和民主的。

These features can skew norms

这些特征可以改变常态，

even in research that doesn’t have an obvious link to them.

甚至在这些特征和研究上 没有明显关联时。

Take the famed Muller-Lyer optical illusion:

以著名的穆勒-莱尔错觉举例，

it’s normal to think one of the two lines is longer,

人们通常认为其中 一条线比另一条长，

when they’re actually the same length.

但是它们其实是一样长的。

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

至少对一个美国大学生 来说是这样的。

A team of anthropologists and psychologists

一个由人类学家 和心理学家组成的团队

found other demographic groups were much less susceptible—

发现其他地域的人们 受错觉影响的可能性要小得多，

members of the San people of the Kalahari

像是卡拉哈里地区的桑人，

weren’t susceptible to the illusion at all.

他们完全不受错觉的影响。

When these limited or inaccurate definitions of normal

当这些有限或是不准确 对正常的定义，

are used to make decisions that impact people’s lives,

被用来制定影响人们生活的政策，

they can do real harm.

这可以导致真实的伤害。

Historically, such concepts of normal have been hugely influential.

历史上，关于正常的定义 有深大的影响性，

The Eugenics Movement of the early 20th century weaponized the concept of normal,

像是20世纪初的优生运动

使用正常的定义作为武器，

using it to justify exclusion, violence, and even extermination

用来合理化排斥、暴力伤害、甚至灭绝

of those deemed not normal.

那些被认为不正常的人。

To this day, people are often targeted and discriminated against

时至今日，人们常受到攻击和歧视，

on the basis of disabilities, mental health issues, sexual orientations,

因为他们的精神问题、性取向、

gender identities, and other features deemed “not normal.”

性别认同和其他被认为 “不正常”的特征。

But the reality is that the differences in our bodies, minds,

事实上，这只是我们 身体、思想、认知

perceptions, and ideas about the world around us—

和对周围世界的想法上的差别。

in short, diversity— is the true normal.

简而言之，多样性 才是真正的“正常“。