How to spot a misleading graph - Lea Gaslowitz

譯者: Sherl H 審譯者: 庭芝 梁

A toothpaste brand claims their product will destroy more plaque

某個牙膏品牌宣稱他們的產品 能消滅的牙菌斑數量

than any product ever made.

比有史以來任何產品更多

A politician tells you their plan will create the most jobs.

一位政治家告訴你 他的計畫能產生最多就業機會

We're so used to hearing these kinds of exaggerations

在廣告和政治活動中

in advertising and politics

我們對於這類的誇飾習以為常

that we might not even bat an eye.

甚至眼睛連眨都不眨一下

But what about when the claim is accompanied by a graph?

但如果提出的論點搭配了圖表呢？

Afterall, a graph isn't an opinion.

畢竟，圖表並非只是個人的觀點

It represents cold, hard numbers, and who can argue with those?

它呈現出實際、明確的數字 而誰能夠質疑這些數字？

Yet, as it turns out, there are plenty of ways graphs can mislead

是的，事實證明 圖表有很多方法誤導他人

and outright manipulate.

甚至是肆無忌憚地進行操弄

這裡有幾件事需要密切注意

Here are some things to look out for.

In this 1992 ad, Chevy claimed to make the most reliable trucks in America

在這則 1992 年的廣告中 雪佛蘭汽車使用這張圖表

宣稱他們製造出全美國最可靠的貨車

using this graph.

Not only does it show that 98% of all Chevy trucks sold in the last ten years

它不僅顯示過去十年來 雪佛蘭銷售的貨車

有 98% 還能在路上行駛

are still on the road,

but it looks like they're twice as dependable as Toyota trucks.

而且可靠性看起來像是 豐田貨車的兩倍

That is, until you take a closer look at the numbers on the left

似乎如此 但是當你仔細看左邊的數字

and see that the figure for Toyota is about 96.5%.

可以注意到 豐田的數據 大概是 96.5%

The scale only goes between 95 and 100%.

整個圖表的刻度範圍 是 95%～100%

If it went from 0 to 100, it would look like this.

如果將範圍改成 0～100% 看起來就會像這樣

This is one of the most common ways graphs misrepresent data,

用圖表來曲解資料時

最常見的方式之一 就是扭曲刻度範圍

by distorting the scale.

Zooming in on a small portion of the y-axis

放大 y 軸的一小部分

exaggerates a barely detectable difference between the things being compared.

會放大項目之間 幾乎無法察覺的微小差距

And it's especially misleading with bar graphs

而且長條圖特別容易產生誤導

since we assume the difference in the size of the bars

因為我們會認定長條的面積

與其數值大小成等比關係

is proportional to the values.

But the scale can also be distorted along the x-axis,

但 x 軸尺度也可能被扭曲

usually in line graphs showing something changing over time.

通常是想要在曲線圖中 顯示某件事隨著時間而改變

This chart showing the rise in American unemployment from 2008 to 2010

這張圖顯示 2008 到 2010 年間 美國失業率的攀升

manipulates the x-axis in two ways.

圖中用了兩種方式來操弄 x 軸

First of all, the scale is inconsistent,

首先，刻度範圍並不一致

compressing the 15-month span after March 2009

從 2009 年 3 月 之後的 15 個月被壓縮

to look shorter than the preceding six months.

使其看起來比前面的六個月更短

Using more consistent data points gives a different picture

當我們用前後一致的資料點 會得到截然不同的圖形

with job losses tapering off by the end of 2009.

失業率在 2009 年底逐漸停止攀升

And if you wonder why they were increasing in the first place,

而如果你想瞭解 為何一開始失業率是上升的

the timeline starts immediately after the U.S.'s biggest financial collapse

那是因為時間軸的起點正好緊接著

美國從經濟大蕭條以來 最嚴重的一次金融風暴

since the Great Depression.

These techniques are known as cherry picking.

這種手法被稱為「採櫻桃謬誤」

A time range can be carefully chosen to exclude the impact of a major event

就是小心地選擇某一段時間

並且將這段時間之外的 重要影響因素排除

right outside it.

And picking specific data points can hide important changes in between.

而且，挑選某些特定的資料點 還能隱藏這段期間的重要變化

Even when there's nothing wrong with the graph itself,

即使圖表本身沒有錯誤

leaving out relevant data can give a misleading impression.

刻意省略相關資料 也會產生誤導他人的印象

This chart of how many people watch the Super Bowl each year

這張圖表是關於 每年有多少人觀看超級盃比賽

makes it look like the event's popularity is exploding.

看起來超級盃的受歡迎度 似乎每年急遽上升

But it's not accounting for population growth.

但它沒有將人口數的增加列入計算

The ratings have actually held steady

實際上，收視率一直維持穩定

because while the number of football fans has increased,

因為足球迷的人數雖然增加

their share of overall viewership has not.

但是佔所有觀眾的比例卻沒有改變

Finally, a graph can't tell you much

如果你不能充分理解 圖表所呈現資訊的真正意義

if you don't know the full significance of what's being presented.

圖表無法告訴你很多事情

Both of the following graphs use the same ocean temperature data

以下兩張圖同樣都使用

來自國家環境資訊中心的 海洋溫度資料

from the National Centers for Environmental Information.

So why do they seem to give opposite impressions?

為什麼會帶給人們 截然不同的感覺呢？

The first graph plots the average annual ocean temperature

第一張圖所畫的是

從 1880 到 2016 年的 年平均海洋溫度

from 1880 to 2016,

making the change look insignificant.

看起來變化並不顯著

But in fact, a rise of even half a degree Celsius

但事實上，即使只是增加攝氏 0.5 度

can cause massive ecological disruption.

就可能造成嚴重的生態破壞

This is why the second graph,

這就是為什麼在第二張圖中

which show the average temperature variation each year,

顯示的每年平均溫度變化

is far more significant.

看起來更為明顯

When they're used well, graphs can help us intuitively grasp complex data.

當運用得當時，圖表能幫助我們 憑直覺就能瞭解複雜的資料

But as visual software has enabled more usage of graphs throughout all media,

但是當影像軟體普及 圖表就更常被用在各種媒體中

it's also made them easier to use in a careless or dishonest way.

同時也更容易 被以草率或欺騙的方式運用

So the next time you see a graph, don't be swayed by the lines and curves.

所以下次當你看到圖表時 不要被線條和曲線所操弄

Look at the labels,

看清楚標示

the numbers,

數字

the scale,

量測刻度

and the context,

以及前後脈絡

and ask what story the picture is trying to tell.

並且提出質疑： 這張圖到底想表達什麼？