For data to be impactful, it must be understood.

為了使數據具有影響力，必須理解它。

I’ve happily spent hundreds and hundreds of hours of my life watching users misunderstand data visualizations. I’m strangely hooked on it.

我快樂地度過了數百個小時的人生，看著用戶誤解了數據可視化。 我很迷上它。

It’s fascinating how even the simplest charts and graphs can be so frequently misinterpreted. And I’m not talking about Fox News’ crimes against y-axes, which are purposefully designed to mislead. I mean, for example, basic bar charts, composed by smart designers, that still lead some otherwise smart user to an unexpected interpretation of the underlying data.

令人著迷的是， 即使最簡單的圖表也是如此頻繁地被誤解 。 我不是在談論福克斯新聞(Fox News)針對y軸的犯罪，這些犯罪是有意誤導的。 我的意思是，例如，由聰明的設計師組成的基本條形圖， 仍然會導致一些聰明的用戶對基礎數據進行意外的解釋。

The first challenge: The tiniest design elements can unexpectedly impact users’ mental models and cause irrecoverable confusion. Many talented practitioners offer principles for presenting data visually (see: Cairo, Knaflic, Ware, Tufte). But with so many moving parts to consider, it’s impossible to predict the combined effect without actually testing it.

第一個挑戰：最微小的設計元素會意外地影響用戶的思維模型，并導致無法挽回的混亂。 許多有才華的從業者提供視覺上呈現數據的原則(見： 開羅 ， Knaflic ， 潔具 ， 塔夫特 )。 但是要考慮的運動部件太多，因此如果不進行實際測試就無法預測組合效果。

The second challenge: Per Ben Cooley: “building data visualization products with the purpose of delivering insight is fundamentally different” from other UX (src). So it follows that it requires a different approach to validate.

第二個挑戰： Per Ben Cooley：“與其他UX( src )相比，“構建旨在提供洞察力的數據可視化產品根本不同”。 因此，這就要求使用不同的方法進行驗證。

If we want data to be impactful, it has to be understood. If we want it to be understood, it must be tested with real users. If we want to user test data visualization, we need to consider more than just usability.

如果我們希望數據具有影響力，則必須理解它。 如果我們希望它被理解，則必須經過真實用戶的測試。 如果我們要用戶測試數據可視化，我們不僅需要考慮可用性。

用戶測試數據可視化時要尋找什么？ (What to look for when user testing data visualizations?)

In testing visualizations, we’re typically checking for:

在測試可視化時，我們通常會檢查：

• Comprehension: The visualization communicates what we think it does

  理解 ：可視化傳達了我們認為的功能

• Explainability: Users can relate the visualization to the underlying phenomena being measured (e.g. what’s happening in the real world?)

  可解釋性 ：用戶可以將可視化與正在測量的潛在現象相關聯(例如，現實世界中正在發生的事情？)

• Affect: Users understand conclusions intellectually and they can feel it in their gut. It instills an appropriate sense of urgency.

  影響：用戶可以智能地理解結論，并且可以在自己的直覺中感受到它。 它灌輸了適當的緊迫感。

• Actionability: It nudges users towards a specific course of action.

  可操作性 ：它將用戶推向特定的操作過程。

The questions below are my toolkit for validating these criteria.

以下問題是我用于驗證這些條件的工具包。

A controversial “tornado plot” highlighting cyclical change rates of Covid-19 deaths, fromDanny Dorling. You can see several bewildered onlookers answer “What are you seeing here?” on ChartChat, Twitter and Reddit.有爭議的“龍卷風圖”突出顯示了來自丹尼·多林 ( Danny Dorling)的Covid-19死亡的周期性變化率。 您會看到一些困惑的旁觀者回答“您在這里看到什么？” 在ChartChat ， Twitter和Reddit上 。

1.“您在這里看到什么？” (1. “What are you seeing here?”)

“People often ask me: ‘What’s the most important thing I should do if I want to make sure my Web site is easy to use?’ The answer is simple. It’s not “Nothing important should ever be more than two clicks away,” or “Speak the user’s language,” or even “Be consistent.” It’s… ‘Don’t make me think!’” (Steve Krug, “Don’t Make Me Think”)

人們經常問我：'如果要確保我的網站易于使用，我應該做的最重要的事情是什么？' 答案很簡單。 不是“沒有什么重要的事情可以相隔兩下單擊”，“說出用戶的語言”甚至“保持一致”。 是…… “別讓我思考！” ”(史蒂夫·克魯格，“ 別讓我思考 ”)

What was true for Steve Krug in 2000 is still true for design today: To make something accessible, optimize for reducing cognitive load.

史蒂夫·克魯格(Steve Krug)在2000年的情況仍然適用于當今的設計：要使某些東西易于使用，請進行優化以減少認知負擔。

This may seem unintuitive for tools that only exist for navigating complexity. You might even say “but this data is meant to be thought-provoking!” But just because the story is complex, doesn’t mean the presentation should be.

對于僅用于導航復雜性的工具而言，這似乎并不直觀。 您甚至可能會說“但這些數據真是發人深省！” 但是，僅僅因為故事很復雜，并不意味著演示應該如此。

To validate this, you want to test what users conclude from a visualization if you weren’t sitting there with them. (Steve calls this “get it” testing.)

為了驗證這一點，如果您不與用戶坐在一起，您想測試用戶從可視化中得出的結論。 (史蒂夫稱此為“得到”測試。)

So, just like you would in a typical user test, start with open-ended questions like “What are you seeing here?” or “What is this graph trying to tell you?” It’s a gentle nudge to see how users respond, free of any prompting, priming or bias

因此，就像在典型的用戶測試中一樣，從諸如“您在這里看到什么？”之類的開放性問題開始。 或“此圖試圖告訴您什么？” 看到用戶如何回應，沒有任何提示，啟動或偏見，這是一種微妙的推動

This gives you a signal of what captures their attention and the conclusions they might draw on their own.

這給您一個信號，說明吸引他們注意的是什么，以及他們可能自己得出的結論。

What to look for?

要找什么？

• Good: They quickly recite the intended conclusions, in priority order. They’re not squinting. “I did a good job today, I walked 10k steps”

  良好 ：他們Swift按優先順序背誦預期的結論。 他們沒有斜視。 “今天我做得很好，走了1萬步”

• Trouble: They’re distracted by minutia. They imagine spurious contradictions. “This says I walked 2km. Is that today or this week? I only burned 500 calories, but it’s green so I guess that’s good? Also, how long is a km again?”

  麻煩 ：他們對細節有分心。 他們想象虛假的矛盾。 “這說我走了2公里。 是今天還是本周？ 我只燃燒了500卡路里，但是它是綠色的，所以我猜那很好嗎？ 還有，一公里又要走多久？”

• Tip: 50% of people will recite literally what they’re seeing on the screen (e.g. “I see a blue chart that says “# of steps today. Then a green graph that says “# calories. I like the colors!”). Just re-ask a different way to nudge toward interpretations (e.g. “What is this screen trying to tell you?”).

  提示 ：50％的人會按字面背誦他們在屏幕上看到的內容(例如“我看到一個藍色圖表，上面寫著“今天的步數。然后是綠色圖表，上面寫著”＃卡路里。我喜歡這些顏色！” ) 。 只是重新提出一種微妙的解釋方式(例如“此屏幕試圖告訴您什么？” )。

Ambulatory Glucose Profile (AGP)動態血糖曲線(AGP)

2.“最好的[x]是什么？ 最糟糕的[x]是什么？” (2. “What was the best [x]? What was the worst [x]?”)

To be truthful and revealing, data graphics must bear on the question at the heart of quantitative thinking: “Compared to what?” (Edward Tufte, “The Visual Display of Quantitative Information”)

為了真實和揭示性，數據圖形必須在定量思考的核心問題上：“ 與什么相比？ ”(Edward Tufte，“ 定量信息的可視化顯示 ”)

The whole point of visualization is comparing quantities visually. Our brains understand geometry better than abstract numbers, so we visualize.

可視化的重點是可視化比較數量。 我們的大腦比抽象數字更了解幾何，因此我們進行了可視化。

Testing is simple: Ask users questions about the data. One of the more reliable: ask them to identify the extremes. (e.g. “What day was your healthiest day? What day was your least healthy?” or “When did you make the most sales? When was the slowest?”)

測試很簡單：向用戶詢問有關數據的問題。 較可靠的方法之一：請他們確定極端情況。 ( 例如，“哪一天是您最健康的一天？哪一天是您最不健康的一天？”或“您什么時候銷售額最高？什么時候最慢？”)

You might also consider phrasing this in terms of value judgements (e.g. “best, worst”, not “largest, smallest”), to make sure they’re able to differentiate quantities and draw appropriate conclusions. This is particularly important when “good” isn’t necessarily the highest or lowest values presented (e.g. blood-glucose levels).

您也可以考慮根據價值判斷(例如“ 最佳，最差 ”，而不是“最大，最小”)來表述，以確保他們能夠區分數量并得出適當的結論。 當“好”不一定是所呈現的最高或最低值(例如血糖水平)時，這一點尤其重要。

You can also follow this up with similar quiz-like questions, such that, if they understand the data, they should be able to easily answer the question (e.g. “During which period(s) was there a decline in the number of births?”).

您還可以通過類似類似測驗的問題來跟蹤問題，例如，如果他們了解數據，他們應該能夠輕松回答該問題(例如， “在哪個時期出生人數下降了？ ” )。

Hearst et. al. explore this further and find that having people answer questions about complex visualizations helps make designers “aware of the problems that might arise in actual use of their design.” (Hearst, Laskowsk, Silva, “Evaluating Information Visualization via the Interplay of Heuristic Evaluation and Question-Based Scoring”)

赫斯特等 等 進一步探索這一發現，發現人們回答有關復雜可視化的問題有助于使設計師“意識到在實際使用他們的設計時可能出現的問題。” (Hearst，Laskowsk，Silva，“ 通過啟發式評估和基于問題的評分的相互作用來評估信息可視化 ”)

Answers to these questions might be obvious when testing simple representations, like a single bar chart. But the more you stray from simple representations, the more they’ll struggle. For example, see NYTimes’ exploration of Elizabeth Warren’s 2019 policy proposal costs (Thomas Kaplan, Aliza Aufrichtig, Derek Watkins, “How Would Elizabeth Warren Pay for Her Sweeping Policy Plans?”). You could expect people to struggle with determining if “Housing” or “Other” are the smallest portions. That’s not always a bad thing, it just depends on the level of precision required to tell the story.

在測試簡單表示形式(如單個條形圖)時，這些問題的答案可能很明顯。 但是，您對簡單表示形式的迷戀越多，他們就會越努力。 例如，請參閱《紐約時報》對伊麗莎白·沃倫(Elizabeth Warren)2019年政策建議成本的探索(Thomas Kaplan，Aliza Aufrichtig，德里克·沃特金斯(Derek Watkins)，“ 伊麗莎白·沃倫(Elizabeth Warren)將如何支付其全面的政策計劃？ ”。 您可能希望人們在確定“房屋”或“其他”是否是最小的方面上費勁。 這并不總是一件壞事，它僅取決于講故事所需的精確度。

What to look for?

要找什么？

• Good: They should be able to answer these quickly. They can spot ambiguity, if it exists. There’s little trouble relating quantities to labels.

  很好 ：他們應該能夠Swift回答這些問題。 他們可以發現歧義(如果存在)。 將數量與標簽相關聯幾乎沒有問題。

• Trouble: More squinting. Mistaking highs for lows. Tracing with fingers.

  麻煩 ：斜眼看。 高低錯位。 用手指追蹤。

User Responses to 2 variations of New US Covid Cases per Day (7-Day Avg), from March 14 to May 14用戶對3月14日至5月14日每天2種新的美國Covid案例(平均7天)的響應

3. “是什么導致數據具有這種形狀？ 現實生活中發生了什么，影響了您在這里看到的數字？” (3. “What caused the data to have this shape? What happened in real life that affected the numbers you’re seeing here?”)

“Abstraction makes it harder to understand an idea and to remember it. It also makes it harder to coordinate our activities with others, who may interpret the abstraction in very different ways.” (Chip & Dan Heath, “Made to Stick”)

“抽象使理解和記住它變得更加困難。 這也使得與他人的活動協調變得更加困難，而他人可能以非常不同的方式來解釋抽象。” (Chip和Dan Heath，“ 堅持做下去 ”)

Data visualization is, by nature, abstract. So becoming too abstract is a persistent risk. This makes it difficult for people to relate the visualization with what they might experience in real life, ultimately limiting the impact of the data.

數據可視化本質上是抽象的。 因此，變得過于抽象是一種持續的風險。 這使人們很難將可視化與他們在現實生活中可能遇到的事情聯系起來，最終限制了數據的影響。

For example, the Covid Tracking Project recommends against abstracting away death counts that mute the emotional impact of Covid-19 deaths.

例如，Covid Tracking Project建議不要提取使Covid-19死亡的情感影響靜音的死亡計數。

“We recommend using total numbers for plotting deaths to compare one US state or territory against another. In this case, adjusting per capita adds a layer of abstraction to the graphic. This reduces the data’s power and the reader’s comprehension.” (The Covid Tracking Project, “Visualization Guide”)

“我們建議使用總數來繪制死亡人數，以將一個美國州或地區與另一個州或地區進行比較。 在這種情況下，調整人均會在圖形上添加抽象層。 這降低了數據的功能和讀者的理解力。” (Covid跟蹤項目，“ 可視化指南 ”)

But it’s not just abstraction that can mask the underlying phenomenon.

但是，不僅僅是抽象可以掩蓋潛在的現象。

Sometimes telling the complete story requires additional context. For example, charts from St Louis Fed’s FRED show overlays for recessions, giving additional context to properly interpret the data.

有時，要講述完整的故事，還需要其他上下文。 例如， 圣路易斯聯儲FRED的圖表顯示了經濟衰退的疊加，為正確解釋數據提供了額外的環境。

Or, sometimes the complete story can be hidden under a homogenous curve. For example, if you were to look at the curve of US Covid-19 cases, you might think the case-count is declining across the country. In reality, most of that drop is due to just 2 states (New York and New Jersey), while case-counts across the rest of the country remain relatively flat.

或者，有時整個故事可以隱藏在同一個曲線下。 例如，如果您查看美國Covid-19案件的曲線，您可能會認為全國的案件數量正在下降。 實際上，下降的主要原因是只有兩個州(紐約州和新澤西州)，而全國其他地區的病案數卻相對平穩。

By asking people to give an explanation for the underlying phenomenon, you can determine how easily they relate the data to their own lives. This can indicate the need to simplify, decompose or supplement the visualization with added context.

通過要求人們對潛在現象進行解釋，您可以確定他們將數據與自己的生活聯系起來的難易程度。 這可能表明需要通過添加上下文來簡化，分解或補充可視化內容。

What to look for?

要找什么？

• Good: They relate the data back to personal experiences (if personal data), they can offer plausible explanations (e.g. “This week was probably bad for Snow Cone sales because it’s December and it’s 27 degrees outside?”), or at least they can interrogate the data to learn more (e.g. tapping a bar on a bar chart to decompose it).

  很好 ：他們將數據與個人經歷(如果是個人數據)聯系起來，可以提供合理的解釋(例如“本周可能對Snow Cone的銷售不利，因為它是12月，外面是27度？” )，或者至少可以詢問數據以了解更多信息(例如，點擊條形圖上的條形圖進行分解)。

• Trouble: Non-answers. They say “well it depends…” (indicating ambiguity). They hunt around for context clues but come up short.

  故障 ：無答案。 他們說“這取決于……”(表示模棱兩可)。 他們四處尋找上下文線索，但很短。

User responses to Jawbone’s classic “UP Coffee” App, tracking users’ caffeine consumption.用戶對Jawbone經典的“ UP Coffee”應用程序的React，跟蹤用戶的咖啡因消耗量。

4.“總體而言，這是好事還是壞事？” (4. “Overall, is this good or bad?”)

“Because visceral design is about initial reactions, it can be studied quite simply by putting people in front of a design and waiting for reactions. (Donald Norman, “Emotional Design: Why We Love (or Hate) Everyday Things”)

“由于內臟設計是關于初始React的，因此可以很容易地通過將人們置于設計之前并等待React來研究它。 (唐納德·諾曼，“ 情感設計：為什么我們愛(或恨)日常事物 ”)

To persuade, it’s important to appeal to audiences both rationally and emotionally. Data, when well-presented, can accomplish both. To do this, the visualization should create a sense of tension by highlighting the difference between “this is how things are now” and “this is how much better they could be.”

要說服，重要的是要在理性和情感上吸引觀眾。 數據呈現得當，可以同時實現兩者。 為此，可視化應該通過強調“這就是現在的狀況”與“這就是 現在的狀況 可以更好”之間的差異來營造一種緊張感。

This works for both for personal, performance visualizations (e.g. Fitbit creates tension between your current step count and the magical 10,000 steps) and for more objective, journalistic work (e.g. looking at this graph, Americans might feel tension: “Denmark, Italy and Canada are beating the US in testing?”).

這既適用于個人績效可視化(例如Fitbit在您當前的步數和10,000神奇的步數之間產生張力)，也適用于更客觀的新聞工作(例如， 查看此圖，美國人可能會感到緊張：“丹麥，意大利和加拿大在測試中擊敗美國？” )。

When this is well-executed, you can expect an almost visceral reaction from your users. Not only do they intellectually understand the “gap,” they feel it.

如果執行得當，您可以期待用戶的幾乎內在的React。 他們不僅在理智上理解“差距”，而且感覺到了。

To verify this, simply ask your test subjects to judge for themselves. (e.g. “Do you think this is good or bad?”)

要驗證這一點，只需讓您的測試對象自己判斷即可。 (例如， “ 您 認為這是好是壞？” )

What to look for?

要找什么？

• Good: At the very least, they should be able to identify the “gap” and say whether the data presents a scenario that is good or bad. Ideally, they can speak to the magnitude of the difference. Even better, their own emotional arousal matches that magnitude.

  良好 ：至少，他們應該能夠識別“差距”，并說出數據呈現的是好是壞。 理想情況下，他們可以說出差異的大小。 甚至更好的是，他們自己的情感喚醒達到了這種程度。

• Trouble: Hesitation. Guessing. Mistakes. Apathy.

  故障 ：猶豫。 猜猜 錯誤。 冷漠

Harry Nicholls could tell you how these scenarios represent “Harry Nicholls這樣的專家可以告訴您這些場景如何表示“ 7 Popular Technical Indicators” and translate them into action (to Increase Your Trading Profits).7個流行技術指標”并將其轉化為行動(以增加您的交易利潤)。

5.“下一個[周/月/年]，您可能有何不同的方法來更改這些數字？ 這些信息將如何影響您的方法？ ” (5. “What might you do differently to change these numbers next [week/month/year]? How would this information influence your approach?”)

“There are really only three basic reasons why information ever has value to a business: 1. Information reduces uncertainty about decisions that have economic consequences. 2. Information affects the behavior of others, which has economic consequences. 3. Information sometimes has its own market value.” (Douglas Hubbard, “How to Measure Anything”)

“實際上，信息對企業有價值的原因只有三個基本原因：1.信息減少了具有經濟后果的決策的不確定性。 2.信息影響他人的行為，具有經濟后果。 3.信息有時具有自己的市場價值。” (Douglas Hubbard，“ 如何測量任何東西 ”)

Data is “actionable” if a reasonably informed user would do something different as a result of consuming it. This is what Hubbard’s 1st and 2nd reasons refer to.

如果合理告知的用戶由于使用數據而做其他事情，則數據是“可操作的”。 這就是哈伯德的第一個和第二個原因。

Testing for “actionability” is important for 2 reasons:

測試“可操作性”很重要，原因有兩個：

You want to make sure the visualization helps users understand the data well-enough to inform their future actions. This is a prerequisite for users to act. (i.e. The steering wheel is useless if you can’t see through the windshield.)

您要確保可視化可以幫助用戶充分理解數據，以告知其將來的操作。 這是用戶采取行動的前提。 (即，如果看不到擋風玻璃，方向盤就沒用了。)

You also want to make sure you’re not overloading people with useless data (i.e. data vomit). If data doesn’t inform some decision or behavior, you probably don’t need to visualize it.

您還想確保您不會使無用數據的人(例如數據嘔吐)超載。 如果數據無法告知某些決策或行為，則可能無需可視化。

Validating actionability is slightly more tricky.

驗證可操作性比較棘手。

First, identify several scenarios where, given scenarios A, B, and C, a reasonably informed person would tell you to do X, Y, Z. (e.g. “Thermometer says 0 degrees” → “Wear a coat”; “Thermometer says 100 degrees” → “Stay inside”).

首先，確定幾種情況，在給定情況A，B和C的情況下，有足夠知識的人會告訴你做X，Y，Z。(例如“溫度計表示0度”→“穿外套”；“溫度計表示100度”→“待在里面”)。

Second, make sure you’re testing with “reasonably informed” testers. This is important: Even an objectively effective visualization won’t seem actionable to users who are inexperienced in the problem domain (e.g. the trading graphs above look like nonsense to me, but that doesn’t mean they’re ineffective for technical traders). Unless these non-experts are your audience, they’re only going to give you false negatives. So make sure you’re testing with folks who would know what to do with the information (i.e. users who, when presented with scenarios A, B and C, know to proceed with actions X, Y and Z)

其次，確保您正在與“合理告知”測試人員進行測試。 這一點很重要：即使是客觀有效的可視化對于那些在問題領域沒有經驗的用戶來說似乎也不可行(例如，上面的交易圖對我來說似乎是胡說八道，但這并不意味著它們對技術交易者無效)。 除非這些非專家是您的聽眾，否則它們只會給您帶來虛假的負面評價。 因此，請確保您正在與知道如何處理信息的人員進行測試(即，當用戶在遇到場景A，B和C時，他們知道要進行操作X，Y和Z的用戶)

Once you have your scenarios and reasonably informed test users, walk the testers through each scenario, as represented by the visualization, and ask them what they would do. Their answers might differ (e.g. “100 degrees” might mean “stay inside,” “wear sunscreen,” “stay hydrated”), but each of the responses should indicate whether or not the person knows it’s hot enough that they should do something different.

一旦有了場景并合理地通知了測試用戶，就可以按照可視化表示的方式逐步引導測試人員完成每個場景，并詢問他們將要做什么。 他們的回答可能有所不同(例如，“ 100度”可能表示“待在里面”，“戴防曬霜”，“保持水分”)，但是每個回答都應表明該人是否知道自己的溫度足夠高以至于他們應該做些不同的事情。

What to look for?

要找什么？

• Good: Your experts can translate the scenario you’re presenting to a specific course of action.

  很好 ：您的專家可以將您要呈現的場景轉換為特定的操作過程。

• Trouble: “I don’t know.” This can indicate a) the person isn’t an expert or b) the data presented isn’t sufficient to determine an action or c) the data wouldn’t actually have any bearing on downstream actions. (C. is the one to watch out for. That’s the sad path to data-vomit.)

  麻煩 ： “我不知道。” 這可能表明a)該人不是專家，或者b)所提供的數據不足以確定一項行動，或者c)數據實際上對下游行動沒有任何影響。 (C.是要提防的那個。這是通往數據嘔吐的悲傷之路。)

• Tip: Initial responses will sometimes be vague (e.g. “if the cookies aren’t selling, those lazy girl scouts need to try harder.”), so be ready to follow it up (e.g. “Sure. But based on what you’re seeing here, is there something else they might try?”) Then they might say “Oh, I see all they have left are Trefoils. No one eats that garbage. Get those girls some Thin Mints!”

  提示 ：最初的回應有時會含糊不清(例如“如果餅干不賣，那些懶惰的女童子軍就需要加倍努力。”)， 準備跟進它(例如“當然。但是，根據您在這里看到的內容，他們還可以嘗試其他方法嗎？” )然后他們可能會說： “哦，我看到所剩下的都是Trefoils。 沒有人吃那垃圾。 給那些女孩買些薄薄的薄荷糖！”

Design is iterative. Data design is no exception. Ask just a handful of test users these 5 questions and, not only will you learn how to improve your visualizations, you’ll also spark great conversations that help you better relate to your users and ultimately make the work more understandable, relatable, impactful and actionable.

設計是迭代的。 數據設計也不例外。 僅向少數測試用戶詢問這5個問題，不僅會學習如何改善可視化效果，還會引發精彩的對話，幫助您更好地與用戶建立聯系，并最終使工作更加易懂，相關，影響力大，可行的。

Who am I? No, no, who are you?!

我是誰？ 不，不，你是誰？

I’m Eli. I help clients use data (visualization) to create positive change by engaging audiences not just rationally, but also emotionally and culturally. Change happens in the head and the heart.

我是伊萊 我幫助客戶使用數據(可視化)，不僅通過理性地，而且在情感上和文化上吸引受眾，來創造積極的變化。 變化發生在頭部和心臟。

If you’re interested in user-testing data viz, or if you’re a founder, maker, storyteller or business leader working at the intersection of data, design and user psychology, I’d love to connect and trade stories.

如果您對用戶測試數據感興趣，或者您是在數據，設計和用戶心理交匯處工作的創始人，制造商，講故事者或業務負責人，那么我很想聯系并交易故事。

You can email me at eli@3iap.co or follow me on Twitter.

您可以通過eli@3iap.co給我發送電子郵件，或者在Twitter上關注我。

翻譯自: https://uxdesign.cc/crucial-questions-for-user-testing-data-visualizations-297413f7d6ab

總結

以上是生活随笔為你收集整理的“这张图告诉你什么？”的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。