Don’t Let Your Brain Deceive You: Avoiding Bias In Your UX Feedback
You know that user feedback is crucial — after all, your users will decide whether your app succeeds or not — but how do you know whether users are being fair and objective in their feedback?
We can tell you: They won’t be. All of your users will be giving you biased feedback. They can’t help it.
When soliciting and listening to user feedback, you will inevitably run into bias on both sides of the coin: Biases will influence the people providing feedback, and your own biases will influence the way you receive that feedback.
It’s important to be aware of this, especially when reviewing comments about your user experience (UX). Accurate and unbiased feedback is essential to developing the best possible version of your app1. Although you can’t erase your own biases (or those of your users), you can take steps to overcome common biases once you know what they are and how they might appear. The next time you ask your users for input, keep bias in mind and evaluate how you respond to users’ comments. Is your action (or inaction) driven by bias?
There are dozens of cognitive biases3 that take many different forms, although a few dominating types emerge frequently for product teams seeking user feedback. In this article, we’ll take a closer look at four of the most common types of cognitive biases that pop up when collecting and interpreting UX feedback — and how you can nip these biases in the bud, before they skew your production process:
- Confirmation Bias4
- Framing Bias5
- Friendliness Bias6
- False-Consensus Bias7
Let’s dig into the first cognitive bias.
Confirmation Bias Link
This is probably the most well-known bias encountered by people of all professions. Psychologist Daniel Kahneman8, who first introduced the concept of confirmation bias together with mathematical psychologist Amos Tversky, says that confirmation bias exists “when you have an interpretation, and you adopt it, and then, top down, you force everything to fit that interpretation.” Confirmation bias occurs when you gravitate towards responses that align with your own beliefs and preconceptions.
Solely accepting feedback that aligns with your established narrative creates an echo chamber that will severely affect your approach to UX design. One dangerous effect of confirmation bias is the backfire effect9, in which you begin to reject any results that prove your opinions wrong. As a designer, you are tasked with creating the UX that best serves your audience, but your design will be based in part on your subjective tastes, beliefs and background. Sometimes, as we learned firsthand, this bias can sneak its way into your process — not so much in how you interpret user feedback, but in how you ask for it.
In the early years of my agency designing web and mobile apps for clients, we used to have our UX designers write user surveys and conduct interviews to get feedback on products. After all, the designers understood the UX like no one else, and, ultimately, they’d be the ones to make any changes. Strangely, after doing this for about a year, we noticed that we weren’t getting a lot of actionable feedback. We began to doubt the value of even creating these surveys. Before tossing them out entirely, we experimented by removing the UX designers from the feedback process. We had one of our quality-assurance (QA) engineers write the user survey and collect the feedback — and we quickly found the results were vastly more interesting and actionable.
Although our UX designers were open to hearing feedback, we realized they were subconsciously formulating survey and interview questions in a manner that would easily confirm their own preconceptions about the design they created. For instance, our UX designers asked, “Did the wide variety of products available make it difficult to find the specific product you wanted?” This phrasing led our respondents to perceive that finding a product was difficult in the first place — leaving no room for those who found it easy to reflect that in their answers. The question also suggested a cause of difficulty (the wide variety of products), leaving no room for respondents to offer other potential reasons for difficulty finding a product.
When our QA engineers took the reins, they wrote the question as, “Did you have any difficulty finding the product you wanted? If so, why?” Having no strong preexisting beliefs about the design, they posed an unbiased question — leaving room for more genuine answers from the respondents who had difficulty finding products, as well as those who didn’t. By following up with an open-ended “Why?,” we were able to collect more diverse and informative answers, helping us to learn more about the various reasons that respondents found difficulty with our design.
Confirmation bias often shows up when one is creating user surveys. In your survey, you might inadvertently ask leading questions10, phrased in a way that generates answers that validate what you already believe. Our UX designers asked leading questions like, “Did the branding provide a sense of professionalism and trust?” Questions like this don’t allow space for users to provide negative or opposing feedback. By removing our designers from the feedback process, the questions naturally became less biased in phrasing — our QA engineers asked non-leading questions like, “What sort of impressions did the app’s look and feel provide?” As a result, we began to see far more objective and truly helpful feedback coming from users.
Avoiding Confirmation Bias Link
Overcoming confirmation bias requires collecting feedback from a diverse group of people. The bigger the pool of users providing feedback, the more perspectives you add to the mix. Don’t survey or interview users from only one group, demographic or background — do your best to get a large sample size filled with users who represent all demographics in your target market. This way, the feedback you receive won’t be limited to one group’s set of preconceptions.
Write survey questions carefully to avoid leading questions. Instead of asking, “How much did you like feature X of the app?,” you might ask, “Rate your satisfaction with feature X on the following scale” (and provide a scale that ranges from “strongly dislike” to “strongly like”). The first phrasing suggests that the user should like the feature in question, whereas the second phrasing doesn’t make an inherent suggestion. Have someone else read your survey questions before sending them out to users, to check that they sound impartial.
UX designers can also avoid falling prey to confirmation bias by using more quantitative data — although, as you’ll see below, even interpretation of numerical data isn’t immune to bias.
Framing Bias Link
Framing bias is based on how you choose to frame the user feedback you’ve received. This kind of bias can make a designer interpret an objective metric in a positive or negative light. Nielsen Norman Group offers a fascinating example11 that describes the results of a user feedback survey in two ways. In the first, 4 out of 20 users said they could not find the search function on a website; in the second, 16 out of 20 users stated that they found the search function.
Nielsen Norman Group presented these two results — which communicate the same information — to a group of UX designers, with half receiving the success rate (16 out of 20) and half the failure rate (4 out of 20). It found that only 39% of those who received the positive statement were likely to call for a redesign, compared to 51% of respondents who received the failure rate. Despite the metric being the same, a framing bias led to these professional UX designers behaving differently.
The presence of framing bias when analyzing data can lead to subsequent effects, such as the clustering illusion15, in which people mistakenly perceive patterns in data that are actually coincidental, or the anchoring effect16, in which people give much more weight to the first piece of data they look at than the rest of the data. These mental traps can influence the decisions you make in the best interest of the product.
Avoiding Framing Bias Link
You can avoid framing bias by becoming more self-aware of how you look at data — and by adding more frames.
For every piece of feedback you assess, ask yourself how you’re framing the data. This awareness will help you learn not to take your first interpretation as a given, and to understand why your perspective feels positive or negative. Then, identify at least one or two alternative frames you could use to phrase the same result. Let’s say one of your survey results shows that 70% of users feel your UI is intuitive. This gives you a surge of pride and validation, but you also recognize that it’s framed as a positive. Using an alternative frame, you write the result again as such: 30% of users do not feel the UI is intuitive. By looking at both frames, you gain a less biased and more well-rounded perception of what this data means for your product.
Be wise enough to admit when you’re unsure of what action to take based on your data. Get a second opinion from someone on your team. If one piece of feedback seems particularly important and difficult to interpret, consider sending out a new survey or gathering more feedback on that topic. Perhaps you could ask those users who don’t feel your UI is intuitive to elaborate on specific aspects of the UI (colors, button placement, text, etc.). What specific, impartial questions could you create to gain deeper insight from users?
Friendliness Bias Link
Of course, you want to be civil and professional with the people who provide UX feedback, but it doesn’t pay to be too friendly. In other words, you don’t want to be so accommodating that it skews their responses.
Friendliness bias — also called acquiescence bias or user research bias17 — occurs when the people providing feedback tell you the answers they think you want to hear. Sometimes, this happens because they think fondly of you18 and respect your professional opinion, but the reason can also be less flattering.
People might tell you what you want to hear because they’re tired of being questioned, and they think positive answers will get them out of the room (or out of the online survey) faster. This is the principle of least effort19, which states that people will try to use the smallest amount of thought, time and energy to avoid resistance and complete a task. This principle has probably already influenced the usability of your UX design, but you might not have considered how it comes into play when collecting feedback.
Whatever the cause, friendliness bias can tarnish the hard work and market research you’ve conducted, giving you ungenuine data you can’t effectively use.
Avoiding Friendliness Bias Link
Friendliness bias can be avoided by removing yourself from the picture, because most people don’t like to give unfavorable feedback face to face.
If gathering UX feedback involves in-person questionnaires or focus groups, have someone outside of your development team serve as a facilitator. The facilitator should make it clear that he or she is not the one responsible for the design of the product. This way, people might feel more comfortable providing honest — and negative — feedback.
Collecting feedback digitally is also a helpful way to reduce the chance of your data being compromised by friendliness bias. People might open up more when sitting behind a screen, because they don’t have to face the reactions of the survey’s providers.
Be mindful, especially if you go the digital route, of survey fatigue20. When you ask too many questions, your users might begin to tire partway through the survey. This can result in people simply selecting answers at random (or choosing the most favorable answers) just to finish faster and expend the least amount of effort. To avoid friendliness bias du