Updated on 30 Sep, 2024
Guides • Devam Ghoghari • 10 Mins reading time
It is good to assume that we go into the UX design and research process with an open mind, free of our biases or personal opinions. However, as impartial as we strive to be, it is impossible to avoid allowing assumptions, preconceptions, and other internalized thought patterns to influence our work.
Recognizing and resolving cognitive biases can help mitigate these negative consequences, resulting in end products aligning with consumers’ demands.
In this post, we will look at cognitive bias, the different types of cognitive biases, and how becoming aware of and correcting them may lead to better products.
Cognitive biases are your brain’s way of using mental shortcuts to reduce cognitive load and make faster conclusions and decisions. They appear in every aspect of our existence, yet we have little or no conscious awareness of their presence.
While cognitive biases typically begin as a well-intentioned hack to assist your brain in digesting a large amount of information rapidly, they can lead to miscalculations about the world, resulting in preconceptions, rigid views, and blind spots.
“A cognitive bias is a systematic deviation from rational judgment, where individuals create their own “subjective reality.”
These biases result from cognitive shortcuts that simplify information processing, often leading to distorted perceptions, inaccurate judgments, and irrational decisions. They are influenced by factors like emotions, social pressures, and past experiences, affecting decisions in various contexts”.
One prominent example in user research is confirmation bias, which occurs when consumers only receive information that validates their opinions. This may manifest as just listening to and sharing information that supports your assumptions while rejecting any fresh knowledge that contradicts them.
Cognitive biases like this are, by definition, subconscious acts or beliefs—but they can significantly impact how we study, create, and construct products.
Psychologists Amos Tversky and Daniel Kahneman were the first to coin the term “cognitive bias,” defining it as “people’s systematic yet faulty patterns or responses to judgment and decision difficulties.”
Tversky and Kahneman developed this definition based on research that demonstrated that because humans are exposed to an enormous amount of perceptual stimuli, information, and decisions, we begin to form patterns in our thinking and reasoning to help us speed up and better understand the world around us—not always accurately. Cognitive biases are our brain’s approach to speeding up decision-making and understanding, which can lead to mistakes.
The ultimate impact of cognitive biases in UX research may cause your team to design the wrong things. If the bad things are built, users may become unsatisfied with your products, resulting in decreased usage, increased churn, and less revenue.
Understanding the problem you are attempting to tackle before beginning a project is critical. Understanding the problem will frequently entail meeting with your stakeholders to determine what information you currently have and need to acquire through study.
While anecdotal evidence can be helpful as qualitative data, it should be used cautiously. This can be one of the first situations where cognitive biases emerge—observe how stakeholders structure their reasoning and where they receive the information for their findings.
If someone cannot reference previous discoveries and explain why they hold the opinions they do, they may be making assumptions that should be challenged.
One potential influence of cognitive biases is their ripple effect on larger research objectives. Suppose biases influence or mislead product teams into addressing the wrong problem or implementing a solution that does not meet a user’s need. In that case, you risk squandering time, money, and resources that may be better employed.
In practice, this could mean asking the incorrect questions to your users or recruiting the wrong people. When creating a research plan, you should always include information about the following:
How product teams convey insights influences how those insights are perceived and interpreted; reporting and delivery propel choices and research ahead. Poorly presented information can lead to misinterpretations regarding participants, product health, and even the overall business.
Despite best attempts, discoveries can always be misinterpreted—you can not control other people’s interpretations, but this is all the more reason to be aware of biases and mindful of how insights are conveyed.
Confirmation bias occurs when we favor information that supports our views or knowledge while ignoring information that does not. It keeps wrapped up in the comfort of familiarity, making it difficult to acknowledge and value new ideas.
Dig dive into the complete confirmation bias guide.
This cognitive bias develops when we choose the anchor on which our decisions and reasoning can be based. The anchor is typically the first information we get, shaping our understanding of the topic. Depending on what that is, this cannot be easy if the initial anchor is not the suitable or most relevant option, as it can throw user research off track.
Consider the framing effect, or the psychological bias behind leading questions, in all aspects of UX research. From scoping your user research questions to preparing your interview guide and sharing insights with stakeholders, the way you structure your questions and insights can influence how others perceive the information.
The framing effect happens when you present information in either a positive or negative light, leading others to perceive it in a specific way.
This common psychological phenomenon occurs due to increased stimuli—and thus better information retention—at either end of an experience compared to the middle. Memory and recall studies show that when participants are asked to remember a list of items, they tend to recall items from the beginning or end of the list more quickly.
Clustering illusions arise when we believe we see patterns in data simply because of how it is presented, such as in groups or over a short span. Our brains are geared toward discovering patterns to be more efficient and make quicker judgments, which leads us to underestimate data variability. However, this can imply identifying anything as a ‘pattern’ when, in reality, it is random information with no pattern.
The empathy gap is an emotional impact that shows our tendency to ignore how our emotional state affects our behavior and decisions. This is a typical bias in which we overestimate our impartiality or emotional “neutrality.” It is widespread in a world where we spend much time using technology, talking online, and feeling alienated from our emotions owing to various distractions.
The false-consensus bias, which is particularly prominent when conducting product research, leads people to assume that their personal experiences are more widespread than they are. It also strongly leads people to believe that others will agree with them, especially if they are familiar with the subject (or product) or are working closely on it.
The peak-end rule suggests that the most memorable part of an experience happens when people feel the most emotion—known as the ‘peak.’ This peak is context-dependent and often occurs at the end of the experience, much like the story’s most emotionally intense climax.
You can use the peak-end rule with the serial-position effect, where placing a highly emotional point in the middle of an event enhances the memorability of the otherwise less memorable middle section.
This cognitive bias describes how the placement of questions might influence the outcomes, depending on how you position them. This bias is pervasive in UX research methodologies such as user surveys, as users tend to answer a question by selecting the first option.
Similarly, question order bias occurs in a live interview when a previous question affects how participants react to the following questions simply due to the questions’ design.
Humans are genetically predisposed to seek others’ approval. We want to be perceived positively, adored, and desired, and we want to avoid feeling ashamed, embarrassed, or useless. This can imply social desirability bias, in which people respond in ways that they believe (consciously or unconsciously) are more socially acceptable and preferable.
A bias sees the individual as the reason for a situation’s outcome rather than its context or circumstances. For example, instead of considering extenuating circumstances or various factors that could explain an event, people often place responsibility on an individual—typically focusing on a specific personality trait or their inherent character.
Hick’s law, sometimes called the Hick-Hyman law, explains that when people face more options or choices, they take longer to make a decision. You can design interfaces that are more user-friendly, effective, and intuitive by taking Hick’s law into account and limiting the options.
As the name implies, the bandwagon effect is a cognitive bias that causes us to adopt or embrace something just because it is trendy. You can see this bias in design trends such as the 1990s’ skeuomorphism, which featured fake realism, the 2000s’ glossy buttons, and the Corporate Memphis illustration style associated with Big Tech corporations in the late 2010s.
While these design strategies aren’t inherently incorrect, their novelty and uniqueness can diminish as they become more common.
This is not it; there are almost 175 cognitive biases; read the complete list of cognitive biases.
Some user testing techniques that can reduce the impact of cognitive biases include:
Giving test participants only five seconds to interact with a design reveals what piques their interest. It can limit the influence of confirmation bias and whether availability bias leaves users with a positive impression.
Like five-second testing, it allows people to interact with a design for a limited time. This is a beautiful technique to assess if you have limited your selections using Hick’s rule and if anchoring bias is influencing your decision.
Having test participants organize, and structure material that might show patterns that go beyond your conclusions is an excellent approach to checking for confirmation bias.
Here’s everything you need to know about card sorting
Awareness of cognitive biases is step one toward eliminating user research prejudice, but you do not have to tackle it alone. Numerous UX research platforms are available to perform objective, accurate research, and you can use tools like usability testing templates to maintain research fair and neutral.
Cognitive bias is the systematic pattern of deviation from rationality in judgment, where inferences about other people and situations may be illogical.
An example of cognitive bias is confirmation bias, where individuals searches for information that confirms their preconceptions. This can lead to them ignoring contradictory information and reinforcing their existing beliefs, even in the face of evidence to the contrary.
To avoid cognitive bias, individuals can utilize strategies such as seeking out diverse perspectives, being aware of potential biases, challenging assumptions, gathering objective data, and considering multiple sources of information before making decisions. Individuals can strive to make more rational and objective judgments by actively recognizing and addressing cognitive biases.
Cognitive bias directs to the systematic pattern of deviation from rationality in judgment, where inferences about other people and situations may be illogical.
Confirmation bias, on the other hand, is a specific type of cognitive bias that involves favoring information that confirms one’s preexisting beliefs or values. In essence, cognitive bias is a broader term that encompasses various biases in decision-making and judgment. In contrast, confirmation bias relates explicitly to seeking information that supports one’s views.
Researchers have documented over 175 cognitive biases, each representing a distinct pattern of deviation in judgment or decision-making. These biases can influence how individuals perceive information, leading to errors in reasoning or decision-making processes. Some common examples of cognitive biases include confirmation bias, availability heuristics, and anchoring bias. Each bias can impact an individual’s perception and behavior in various contexts.
UI UX Designer
Devam Ghoghari, a seasoned UI UX designer at Octet, excels at collaborating with diverse teams, tackling challenges, and delivering high-quality designs.
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