User research is an important cornerstone of design. It helps guide the direction of the product and keeps design concepts in line with user demands. It offers essential information used in designing and developing solutions from the user’s perspective. However, the usefulness of this type of study is only as good as the data collected and analyzed.
One of the significant obstacles in this process is cognition biases that affect decision-making. These are inherent human psychological tendencies that can significantly bias the outcomes of research. If left uncontrolled, these cognitive biases can cause wrong perceptions of users’ behaviors and requirements and lead to wrong decision-making in design solutions.
Eradicating such biases is vital in ensuring that user research is not tainted. This article walks you through how cognitive biases can affect user research and what designers should avoid to mitigate the effects. We will also discuss proven strategies to foster unbiased understanding, which contributes greatly to the creation of better and more user-friendly products.
Breaking down cognitive biases in user research: A quick explanation
Cognitive biases are systematic patterns of deviation from rationality or logical judgment, affecting individuals' decision-making processes. They stem from mental shortcuts, emotional influences, and social factors that often lead to distorted perceptions and judgments.
Cognitive biases can significantly compromise the validity and reliability of user research findings. One of the most common biases observed is confirmation bias, where researchers unconsciously seek out or interpret information in a way that confirms their preexisting beliefs or hypotheses. This can lead to cherry-picking data that supports predetermined design solutions while ignoring contradictory evidence, ultimately resulting in suboptimal outcomes.
Research in psychology has identified a wide array of cognitive biases. The illustration below lists the most common biases, which might surprise you due to their number and diversity. This Cognitive Bias Codex provides a more detailed insight into how much our thinking and reasoning may be manipulated.
However, the implications of many above-listed cognitive biases in user research can be broadly classified as:
Confirmation Bias:
Among the most pervasive biases in user research is confirmation bias. Confirmation biases trigger when individuals seek, interpret, and recall (distorted/cherry-picked) information that confirms their pre-existing beliefs, expectations, and hypotheses. In user design, this bias can lead a UX researcher to neglect contradictory and problematic areas because that confirms their deeply ingrained beliefs. This action of undervaluing information reinforces their initial assumptions and hinders innovation.
Indeed, confirmation bias has a detrimental impact on UX research and design. It can disrupt the practitioners' viewpoints by rejecting alternative possibilities and conflicting opinions, which might soon result in flawed decision-making processes and poor research outcomes.
Nevertheless, when UX professionals acknowledge that they do not have to succumb to confirmation bias and passively work to counteract it, they notice incredible improvements in their decision-making capabilities and research methodologies. This results in superior products and more satisfying user experiences.
Availability Heuristic:
Another common pitfall is the availability heuristic. Individuals often rely on readily available information or examples that come to mind quickly. This bias can distort perceptions of user needs and behaviors, as researchers may prioritize recent or vivid experiences over more representative data.
Critical aspects of the availability heuristic include:
- Ease of recall: In other words, we still tend to provide more favorable consideration to information that one can easily recall or that stands out in one’s consciousness.
- Recent events: New information tends to be easier to recall than old information; therefore, the most recent event may be more important.
- Vivid or emotional experiences: Emotional events, especially dramatic or personally affecting ones, are stored more fully and are therefore more ‘available.’
- Media influence: Some events are always in the media and end up being seen as common or important than they are.
- Personal experience: It would be more personal and could be based on something you or someone else saw, heard, or experienced rather than reference data.
An individual might consider the divorce rate relatively high, although the actual rate is relatively low, if, for instance, he or she knows several individuals who have recently gone through a divorce. The availability heuristic creates biases and wrongly estimates risks. Although it can prove incredibly helpful in decision-making at times, this bias should still be recognized, and unnecessary reliance on it should be avoided, especially when critical decisions must be made.
Anchoring Bias:
Anchoring bias is an output of over-dependence on initial pieces of information (which act as an "anchor") when making decisions or judgments. In user research, this bias can manifest as undue influence from early user feedback or prototype iterations, leading to a reluctance to explore alternative design directions. It is one of the most striking and pervasive cognitive builders that can influence people’s decisions in various spheres.
The anchor helps to set the starting point, and subsequent thoughts and estimates are made with a reference to this starting point, which can often be quite insufficient. What is fascinating about anchoring bias is that it is highly probable and can be observed even when the anchor totally does not relate to the decision that has to be made.
Generally, anchoring bias can be observed in different aspects of a person’s day-to-day existence. For instance, in retailing, the sellers attach the higher price alongside the actual price, knowing fully well that the consumers will be attracted to the middle one. Common knowledge reveals that we first offer profoundly affects negotiations since it acts as an anchor to negotiations substantially. However, anchoring could be involved in professional spheres, legal service offerings, and medical practice. Just as judges might start arriving at a number for a sentence and adjust until they hit a figure which is a multiple of 5, to take a frivolous example, doctors might begin to diagnose an illness and then adjust depending on the first letters of the symptoms or the findings of a first test.
The consequences of the anchoring bias are not limited to an individual's decisions only. In trading markets, it results in wrong stock evaluation because investors use old prices or analog numbers as reference points. In project management, the initial time or cost estimates may set a benchmark that is normally difficult to adjust when the realities of the project really begin to present themselves on the scene. This bias is often in the desperate hands of the marketers and the salespeople who occasionally anchor our perceptions of value with their unending and mind-boggling techniques, such as excessive initial asking prices and countdowns.
Bandwagon Effect:
The bandwagon effect describes the tendency for individuals to adopt certain behaviors or beliefs simply because others do, often without critically evaluating the underlying rationale. This bias can lead to a herd mentality in user research, where researchers conform to prevailing opinions or trends, neglecting divergent perspectives.
Bernard and Killworth also explained that variations of the bandwagon effect in user research relate to participants' conformity biases shaped by the notion of the majority rule or the most popular option. Linguistic variation poses challenges to the reliability of results obtained from users as their response rates may be affected, which in turn may result in skewed data.
When considering the concept of the bandwagon of user research, there are several ways in which it could manifest itself. For example, while conducting focus groups or group interviews, a respondent might be influenced by the ‘majority’ opinion that they have heard from other participants and thus develop a `false consensus.’ During the activities, some participants may find themselves holding back on what they actually feel or encountered to align with what they think other members feel.
In a similar manner, when individuals are involved in usability testing or product assessments, frequently, their responses become influenced by previous performances that they come across or even observe other participants engage in the study and, as a result, change their feedback to fit what they consider appropriate or popular. This can conceal genuine usability problems or individual choices that may be useful in product design.
Nine proven ways to mitigate Cognitive Biases
Faster project turnarounds, consistency, better UX, and customer satisfaction are the four pillars of every valuable design system. Businesses aiming to deliver a consistent user experience and constant digital excellence must follow the below-discussed five-step transformation approach when building a design system.
Given the pervasive nature of cognitive biases, design professionals must employ strategies to mitigate their impact on user research. One approach is to foster awareness and mindfulness among researchers regarding cognitive biases' existence and potential effects. By acknowledging the susceptibility to biases, researchers can adopt a more critical and reflective stance, actively questioning their assumptions and interpretations throughout the research process.
Furthermore, employing diverse research methods and data sources can help counteract the influence of biases by triangulating findings from multiple perspectives. Combining qualitative and quantitative approaches, such as interviews, surveys, observational studies, and analytics data, can provide a more comprehensive understanding of user behaviors and preferences, reducing the risk of bias-induced distortions.
Here are some quick and effective strategies to mitigate cognitive biases in user research.
- Awareness is key
Before embarking on any research endeavor, it's crucial to acknowledge the existence of cognitive biases and their potential impact. By raising awareness among team members, stakeholders, and researchers themselves, you create a collective mindset geared toward recognizing and addressing biases throughout the research process. Encourage open dialogue about biases and their implications to foster a culture of critical thinking within your design team. - Diverse perspectives
To combat biases, embrace diversity in your research team. Different backgrounds, experiences, and viewpoints can offer unique perspectives, helping to uncover blind spots and challenge preconceived notions. Encourage collaboration among interdisciplinary teams comprising individuals with varied expertise, such as psychologists, anthropologists, and data analysts. This multidisciplinary approach fosters richer insights and reduces the influence of individual biases. - Define clear objectives
Establishing clear research objectives serves as a guiding light, steering the research process toward meaningful outcomes. Clearly outline what you aim to achieve through the research and what questions you seek to answer. This clarity keeps the research focused and helps mitigate biases by preventing researchers from veering off track or interpreting data to fit predetermined narratives. - Use structured methods
Employing structured research methods minimizes the potential for bias by providing a systematic data collection and analysis framework. Utilize established techniques such as surveys, interviews, and usability tests, ensuring consistency in approach and reducing the likelihood of subjective interpretation. Structured methods help maintain objectivity and enhance the reliability of research findings. - Triangulation
Triangulation involves corroborating findings from multiple sources or methods to validate research outcomes. Researchers can cross-reference information by triangulating data from different sources – such as interviews, observations, and analytics – identifying discrepancies and patterns that offer deeper insights. This approach strengthens the credibility of research findings and helps mitigate biases arising from reliance on a single data source or method. - Embrace iterative design
Embracing an iterative design process allows for continuous refinement based on user feedback. By soliciting feedback early and often, designers can validate assumptions, uncover user preferences, and iteratively improve the design solution. This iterative approach reduces the risk of biases by incorporating real-world user input throughout the design process, leading to more user-centric and effective solutions. - Empathy and user-centricity
Cultivate empathy for your users and prioritize their needs throughout the design process. By adopting a user-centric mindset, designers can better understand user motivations, behaviors, and pain points, mitigating biases rooted in assumptions or personal preferences. Empathy-driven design encourages designers to step into the shoes of their users, fostering a deeper understanding of their needs and preferences. - Data-driven decision making
Base design decisions on empirical evidence rather than subjective opinions or intuition. Collect and analyze quantitative and qualitative data to inform design choices, prioritizing data-backed insights over personal biases or preferences. By relying on data-driven decision-making processes, designers can mitigate the influence of cognitive biases and ensure that design decisions are grounded in objective evidence. - Critical reflection
Encourage researchers to critically reflect on their biases and assumptions throughout the research process. Regularly revisit research objectives, methodologies, and findings, challenging ingrained biases and seeking alternative perspectives. Foster a culture of self-awareness and reflexivity, empowering researchers to acknowledge and address biases as they arise, ultimately enhancing the validity and rigor of the research.
