設計具說服力的圖表

Designing Persuasive Charts
史考特.貝里納托 Scott Berinato
瀏覽人數:2225


影片載入中...
即使小小的決定,也可以產生大影響。本影片採訪史考特.貝里納托(Scott Berinato),史考特為《哈佛商業評論》英文版資深編輯,著有《好圖表練習本:做出更好的資料圖表的訣竅、工具和練習》(Good Charts Workbook: Tips, Tools, and Exercises for Making Better Data Visualizations, HBR Press, 2019)和《哈佛教你做出好圖表》(Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations)。

莎拉:史考特,很多有趣的心理學說明大腦如何運作。當我們看著圖表時,為什麼會覺得它們很有說服力。你能否為我們簡要說明一下這些研究?

史考特:當然好。現在這方面有很多進展,這個領域變化很快,但很令人振奮。這是很有趣的研究。基本上,關於看圖表時的心理學,最重要的就是很多事情發生得很快,你無法掌控你的大腦對圖表的反應。

這和看書有點不一樣,看書時,我們像是與作者簽了契約,按照作者呈現句子的順序來閱讀。至於圖表,我們的目光直接面對事物,無法阻止一切發生,所以我們會看到顯眼的東西。我們無法不注意到顏色。我們無法不注意到異常值。

一旦發生這種情況,只要我們的目光落在這些事物上,大腦就會立刻開始嘗試形成故事。它開始嘗試從圖表中找到意義。你無法阻止這種情況發生,正因如此,製作圖表時遇到的一些挑戰才會如此棘手,因為一旦你決定採用某種視覺做法,人們就會以某種方式做出反應,無論你是否希望他們這麼做。

莎拉:請說明有哪些關於圖表的問題,可能會欺騙或誤導讀者。

史考特:沒問題。我認為,有時人們確實會惡意使用圖表。他們試圖傳達一個不存在的概念。最近一個例子發生在美國國會,有人出示一張關於計畫生育的圖表,繪出癌症篩檢和墮胎的關係,看起來好像是墮胎已經超過了癌症篩檢,但其實並非如此。這純粹是刻意想要誤導。

莎拉:出現這種情況,原因是否可能並不是想說謊,純粹只是意外?

史考特:我想其實更常見的情況是,人們沒有留意自己使用的工具,或者沒有仔細思考自己呈現資料的方式,結果無意間製作出誤導人的圖表。你看看這張圖,會發現圖中的營收持續成長,看起來不錯,這是很好的走勢,一切都在成長。但如果你再看看Y軸的標示,它顯示的是累計營收。累計的意思是,每年都會加計之前每年的營收。所以,如果我們更改這個圖表,按顏色呈現當年的營收,你就會看見第一年的營收被計算了五次。

第二年的營收被計算了四次,依此類推。所以你看到這樣的成長,但如果我們只繪出當年度的營收,你就會發現,成長情況根本不是那樣,實際上是急劇下降。也許有人試圖哄騙上司,但更有可能的是,他們只是沒有仔細思考自己呈現的內容,以及它真正代表的含義。

莎拉:用資料說故事的方法顯然有很多。有沒有真正客觀的圖表?

史考特:我會說沒有。你可以製作出很誠實、盡量客觀的圖表,但你要知道,所有圖表的製作都牽涉很多主觀的決定。舉例來說,有張圖表的X軸代表年份,從1981、1982到1983年,每個年份之間的間隔多大?圖表應該多寬?圖表的寬度是主觀決定。通常取決於你呈現圖表的媒體、螢幕、手機,而事實證明,這會大幅影響到人們在圖表中看到的內容。

如果你製作的圖表很寬,它會讓曲線變得平坦,這會讓發展走勢看起來和緩,或變化緩慢。如果把它塞進手機又高又窄的螢幕,則會看到更劇烈的變化。對於各年份的間距該多大,沒有正確的答案。這是有點主觀的決定。每個圖表都涉及一些決定,而這些決定會影響人們看到的內容,並影響你訴說的故事。

莎拉:我們剛才談到了X軸。Y軸呢?有哪些常見的問題?

史科特:有幾個。其中最有名的一種是截斷Y軸,也就是Y軸的起點不是零。這樣做的效果是,同樣會創造出很戲劇化的故事,因為你看到的曲線更陡峭,變化更劇烈,因為你從圖表中切出的空白已經不再存在。我們看一下這張圖表,工作滿意度似乎隨著年齡增長而下降。當年歲漸增,你目前的和預期的工作滿意度會持續下降,然後出現這個交叉,看起來非常戲劇化。

但如果我們用完整的Y軸繪出相同的資訊,結果就看起來相當平淡,沒什麼變化,實際上這才是準確的寫照。有人說你絕對不該截斷Y軸,但我認為,這些嚴苛的規則其實沒有用。在某些情況下,截斷是有道理的。科學家有時得處理範圍很有限的資料集,對他們來說,一直研究這些扁平的線毫無幫助。但每次製作圖表時,一定要考慮這些問題。你是否正在誇大發展走勢?你是否因為截斷軸線,而讓情況看起來比實際上變化更大?

莎拉:雙Y軸圖表的情況如何?我承認,我光是閱讀這類型圖表就覺得很難。

史考特:沒錯,雙Y軸可說是更有趣、也更複雜。對我來說,它更常被使用,這點毫無疑問,但它應該像截斷的Y軸一樣受到質疑。就像你在此處看到的,雙Y軸是在同一視覺空間中,衡量兩個不同的事物。就像同一時間在同一個場地打美式足球和踢足球一樣。左側是在衡量特斯拉的市占率從0%到10%。右側是在衡量整體汽車銷量,單位是百萬輛。這兩者完全不同。

兩者在同一空間中,沒有真正可以相互比較之處。但更重要的是,如果你看看綠線,也就是2025年的預測,它達到柱狀體高度的25%左右。這是否意味特斯拉將達到整體汽車銷量的25%?你看一下標示,就知答案為「不是」,其實只占大約2%。因此,我們的估計偏差得離譜。但由於兩者處於相同的視覺空間,所以我們會做比較。我們控制不了這一點。我們的大腦就是這樣運作的。我們看著圖表,試圖從中形成故事。如果我們改用應該採用的方式來畫這個圖表,一種更好的繪製方式,只使用一個衡量指標,也就是車輛總銷量,你會發現完全不同的故事。特斯拉的市占率大幅縮小。

莎拉:你是否能舉一些例子,說明其他常見的抉擇時刻?

史考特:當然可以。一個常見的抉擇時刻,是你選擇要展示哪些資料。以唱片銷售為例,這張圖呈現黑膠唱片銷量的趨勢。每個人都再次認識黑膠唱片。每個人都愛唱片。當然,這張圖顯示黑膠唱片的銷量正在飆升。我認為自這張圖製作以來,黑膠唱片的銷量就一直在成長。但讓我們用相同的資料回溯至1970年代,當時沒有其他的音樂選擇。現在,如果我們只關注2014年的柱狀體,在第一張圖表中,它是最高的柱狀體,也是高峰。

當我們回到1973年,最後那個小點是相同的資料,但呈現出截然不同的情況。如果我們再次選擇更改我們的參考點,不再將黑膠唱片的銷量,與過去和未來的黑膠唱片銷量進行比較,而是與所有音樂銷量比較,就會對2014年的唱片銷量有不同看法。所以,有三種不同方式,可呈現完全相同的資料。

莎拉:有什麼好辦法可以知道,自己是否越線,原本是要讓自己的說法有說服力,結果卻誤導了他人?

史考特:我有一條絕對會遵循的黃金法則,就是如果你在製作圖表,正在看自己的成果,那麼就想想看,自己會不會感覺被騙?如果有人拿這個圖表給我看,而他像我一樣知道全部的資料,像我一樣知道這個圖表的其他版本?我經常用的一個比喻是,你是在放大這個想法,還是在扭曲它?這像是在使用顯微鏡,還是哈哈鏡?我會上當,還是被欺騙?還是會感覺受到操弄?這或許是個明確的訊號,表示你已經越界。

莎拉:史考特,有一點讓我很感興趣,我們一直在談論如何用圖表來說服別人,而又不欺騙他們。但使用圖表的人,也想精明地使用圖表。別人拿圖表給我們看的時候,我們該思考哪些事情?你怎麼知道圖表是否正確?

史考特:這是個好問題,也很難做到。但我認為,隨著我們見過愈來愈多圖表,就會漸入佳境。我認為,看圖表時可以做的第一件事,就是評估一些人們常做的事情。

如果你看到Y軸不是從零開始,你心裡就要知道,這會強化圖表的變化幅度。你必須自問,如果它從零開始,會是什麼情況?你可能無法當場實際執行,但要知道,你看到的故事可能比實際上更加戲劇化。

如果是雙Y軸,這會需要花很多功夫,我可以理解,但你也許應該把這兩個圖表分開,嘗試分別評估它們。不要比較這些值、視覺值,而應只看一個表,並理解它的意思,然後再看另一個,並理解它的意思,之後再嘗試做比較。不要讓視覺比較遮蓋了你要評估的實際值。

莎拉:史考特,非常感謝你今天能來與我分享。

史科特:謝謝,莎拉。

(劉純佑譯)


Sarah: Scott, there's a lot of interesting psychology about what happens in our brains, when we look at charts, why we find them so persuasive. Can you just give us a quick overview of some of that research?

Scott: Sure. And there's a lot going on right now, and the field is changing rapidly, but it's really exciting. It's really interesting research. Basically, the most important thing to know about the psychology of looking at charts is a lot goes on very quickly. And there are things you can't control about how your brain reacts to charts.

It's a little different than reading a book, where we sort of a contract with the author, and we're reading sentences and in order that they're presented to us. With charts, our eyes go straight to things, and we can't stop it from happening, so we see what stands out. We can't help but notice color. We can’t help but notice outliers.

As soon as that happens, as soon as our eyes land on these things, it immediately starts trying to form narratives. It starts trying to make meaning out of it. And you can't really stop this from happening, which is why some of these challenges with building charts are so difficult, because once you decide on a visual approach, people going to react to it a certain way, whether you want them to or not.

Sarah: Tell us a little bit about problems with charts that may lie to people or just mislead them.

Scott: Sure, I think sometimes people do use charts maliciously. That they try to convey an idea that doesn't exist. There was a case recently in Congress, where somebody presented a chart about Planned Parenthood that plotted cancer screenings versus abortions in a way that made it look like abortions have risen above cancer screenings, when that's not the case at all. It was just a deliberate attempt to mislead.

Sarah: So can the same thing happen when it's not actually a lie, but just sort of by accident?

Scott: I would say that's actually more often the case, is that people just aren't paying attention to the tools they’re using, or they’re not thinking critically about the way they're showing their data, and they end up sort of stumbling into a misleading chart. If you look at this here, we see revenue going up in this chart, and it looks fine. It's a nice trend. Everything's rising. But if you look at that y-axis label, it says cumulative revenue. And what cumulative means is every year we're counting all the previous year's revenue. So if we change this chart and show the year's revenue by color, you see the first year's revenue is counted five times.

And the second year's revenues is counted four times, and so forth. So, you've seen this growth, when in fact, if we just plot each year's revenue by itself, you see that growth is not the story at all. It's actually precipitous decline. And so maybe somebody was trying to pull one over on their boss here, but more likely, they just weren't thinking through what they were showing, and what it really represented.

Sarah: There's clearly a lot of ways to tell stories with data. Is there such a thing as a truly objective chart?

Scott: I'm going to argue there isn't. You can make charts very honest and as objective as possible, but when you think about it, there are a lot of decisions that go into every chart that are somewhat arbitrary. So if you think of a chart with years in the x-axis, 1981, 1982, to 1983, how much space goes between years? How wide should that chart be? The width of a chart is an arbitrary decision. It's usually a determined by the media you're presenting on, on a screen, on the phone, and it turns out, this has a dramatic effect on what people see in the chart.

You make a chart very wide, it's going to flatten out curves. It's going to make things look mellow or slower change. If you smoosh that onto a phone, very tall narrow screen, you're going to see much more dramatic change. And there's no right answer on how wide a charge be. It’s somewhat of an arbitrary decision. So every chart has decisions in it which affect what people see and affect the story that you're telling.

Sarah: So, we talked a little bit just now about that x-axis. What about the y-axis? What are some common problems there?

Scott: Well, there's a couple. The most famous one is the truncated y-axis, when people don't start their y-axis at zero. And that has the effect of creating, again, a more dramatic story, because you're actually seeing curves that are steeper, change that is more dramatic, because that empty space that you’ve cut out of the chart isn't there anymore. So if we look at this chart here, it looks like a job satisfaction really goes down over the course of your life. As you get older, your current and expected job satisfaction continues to decline, and then there's this crossover, and it looks pretty dramatic.

But if we chart the same information with the full y-axis, it almost looks unremarkable, flat, and that's actually an accurate portrayal. Some people say you should never truncate the y-axis, but I think those kinds of draconian rules are not actually useful. There are cases where it makes sense. Scientists, sometimes, are dealing with data sets that are in very limited ranges, and it would just not be useful for them to look at these flat lines all the time. But it certainly is something to think about every time you're making a chart. Are you exaggerating trend? Are you making things look more dramatic than they actually are, by truncating your axis?

Sarah: What about the dual Y-axis? Because this is one where, I confess, I have a hard time even reading these charts.

Scott: Yeah, the dual y-axis is actually, in some ways, more interesting and more complicated. To me it's something that's used more often and without question, but it should be question as much as the truncated y-axis. And the dual y-axis, as you can see here, is when you're measuring two different things in the same visual space. So, it's like playing football soccer on the same field, at the same time. So, you have on the left here, we're measuring percentage share for Tesla from 0% to 10%. And on the right, we have millions of total vehicles sold. Those are apples and oranges.

They’re not really comparable in the same space. But what's more, if you look at that green line, the 2025 projection, it reaches about 25% or so up the height of the bar. Does that mean Tesla is going to reach 25% of the total vehicle sales? Well, if you look at the label, no, it’s about 2%. So we're way off here in our estimation. But because they're in the same visual space, we're making the comparison. We can't help it. That’s just how our brains work. We look a chart; we try to form a narrative out of it. If we change this chart to how it should be plotted, a better way to plot it, where we're using a single measure, total vehicle sales, you see the story’s entirely different. Tesla’s share is much smaller.

Sarah: Can you just give us some examples of what some of those other common decision points are?

Scott: Sure, one common decision point would be what data you choose to show. So say we want to talk about record sales, and we have a chart that shows trend in vinyl sales. Everybody knows vinyl’s him again. Everybody loves records. And sure, this chart shows that vinyl sales are spiking. I think they've gone up since even since this chart was made. But let's take that same data, and let's extend it back to the 1970s, when there were no other options for music. And now, if we just focus on the 2014 bar, in that first chart, it's the tallest bar. It's the spike.

When we go back to 1973, that little nub there at the end is the same data represent entirely differently. And then if we, again, choose to change our reference points, and compare vinyl LP sales not to past and future vinyl LP sales, but rather to all music sales, you get another view of the 2014 records sales. So that's three different ways to show the exact same data.

Sarah: So what's a good way to know if you've crossed the line from trying to be persuasive with your case to actually misleading people?

Scott: Well, the rule I always fall back on is the Golden Rule, which is if you're building a chart and you're looking at what you've done, and you think, what I feel duped, if somebody showed this to me, knowing what I know about the full data? Knowing what I know about other versions of the chart I've tried? I often used the metaphor, are you zooming in on the idea or are you distorting it? Is it like using a microscope or a fun house mirror? Would I feel fooled or tricked? Or would it feel manipulative? That's probably a good sign, you've crossed that line.

Sarah: So, Scott, what's interesting here to me is we've been talking about how to persuade people with charts without lying to them. But as a consumer of charts, you also want to be a savvy chart consumer. What are some things to think about if you are being shown charts; how do you know that they're accurate?

Scott: It's a good question and it's hard to do. But I think we're getting better at it all the time as we see more and more charts. And I think the first thing you can really do, as somebody who's looking at a chart, is evaluate some of these common things that people do.

So, if you do see a y-axis that doesn't start at zero, just understand in your mind that that they've heightened the drama. And you have to ask yourself, what would that look like if it actually did start at zero? You may not be able to actually execute that on the spot, but understand that you're seeing a more dramatic story than may exist.

If it's 2 y-axes, and it's a lot of work, I understand that, but you kind of want to pull those two charts apart, and try to evaluate them individually. Don't compare the values, the visual values, look at one and understand what it says, then look at the other and understand what it says, and then try to compare. Don’t let the visual comparision sort of override the actual values that you’re evaluating.

Sarah: Scoot, thank you so much for talking with me today.

Scott: Thanks Sarah.



史考特.貝里納托 Scott Berinato

著有《哈佛教你做出好圖表》


本篇文章主題設計