As a UX researcher, it is important to be aware of cognitive biases that can influence your research process and results. Cognitive biases are mental shortcuts or patterns of thinking that can lead to systematic errors in judgment and decision-making. The biases can also manifest themselves in the design and conduct of studies, in the recruitment and selection of study participants, when talking to users and stakeholders, and in how you interact and interpret, and present the data.
In this blog post, we will discuss five common cognitive biases that UX researchers may experience and provide tips for mitigating these biases: Confirmation bias, availability bias, anchoring bias, hindsight bias, and self-serving bias.
Confirmation Bias
Confirmation bias refers to the tendency to search for, interpret, and remember information in a way that confirms pre-existing beliefs or hypotheses. In other words, humans have a natural inclination to believe that their initial assumptions are true and will try to find any evidence to support this belief. In UX research, confirmation bias can lead to selectively choosing or interpreting data that supports a pre-existing belief or hypothesis while ignoring information that contradicts it.
To mitigate confirmation bias, UX researchers should actively seek out data that challenges their assumptions and hypotheses. By also involving a diverse group of participants and stakeholders in the research process, there are opportunities for different perspectives to emerge and ways to prevent groupthink. When talking to users, researchers should constantly ask "why" to surface the root of their perception within their context. Overall, recognizing the need to be objective and to be aware of confirmation bias is a humbling experience.
Availability Bias
Availability bias is the tendency to overestimate the importance of information that is readily available or memorable. UX researchers may fall prey to availability bias by overemphasizing data that is easy to access or recall, while underestimating less memorable data. This can result in incomplete or skewed research findings, particularly when you overemphasize insights from the most recent participants simply because they were the most recent interviews.
To mitigate availability bias, it's important to establish a systematic collection and analysis process that includes all collected data and data sources, rather than relying on easily accessible or memorable information. This can be challenging, especially when working with tight deadlines and back-to-back user sessions that leave little time for full analysis. Knowing this, try to account for this lack of time from the beginning so that you do not end up shortcutting the data analysis process.
To help address this issue, take 5-10 minutes after each user session to quickly summarize the most important insights gained. This will provide a reference to help better understand and analyze the data later on, and can help avoid overemphasizing data from the most recent participants while underemphasizing data from earlier participants.
Anchoring Bias
Anchoring bias is the tendency to rely too heavily on the research hypotheses or initial information collected when making subsequent judgments, regardless of its accuracy. As a UX researcher, it is critical to not draw premature conclusions based on initial data. It is equally critical to be understand participant perspectives, to be objective, and to discuss findings with stakeholders who may have a broader perspective of the product landscape, Researchers need to remain open to new information and insights throughout the research process. Failing to have an open mind to other possibilities or causes can result in incomplete or biased research findings.
To mitigate anchoring bias, UX researchers should actively seek out alternative explanations or variables that may influence their research findings. This can be accomplished by talking to stakeholders who may have a broader perspective in the product landscape. Additionally, researchers should avoid making premature conclusions based on initial data, and remain open to new information throughout the research process.
Hindsight Bias
Hindsight bias is the tendency to believe, after an event has occurred, that one would have predicted the outcome. As a UX researcher, hindsight bias can lead to overestimating the predictability of research findings. This overconfidence in the ability to predict other future events can lead to risky decisions and bad outcome. More importantly, this bias prevents us from learning from our experiences.
“The core of the illusion is that we believe we understand the past, which implies that the future should be knowable, but in fact we understand the past less than we believe we do.” - p201, Thinking Fast and Slow.
To mitigate hindsight bias, UX researchers should remain that past events do not necessarily predict future outcomes. There may be a variety of factors and considerations that come into play when making connections between the input and resultant actions. Therefore, they should regularly review and reflect on their research methods and findings to identify areas for improvement and future research.
Self-serving Bias
Self-serving bias is the tendency to attribute success to internal factors, such as personal ability or effort, while attributing failure to external factors, such as luck or circumstance. As a UX researcher, self-serving bias can lead to overemphasizing the positive aspects of research findings and downplaying the negative aspects. Although it may seem counterintuitive, delivering bad or unwanted news is important to avoid incomplete or biased research findings.
To mitigate self-serving bias, UX researchers should actively seek out and acknowledge both the positive and negative aspects of their research findings. They should also involve a diverse group of participants and stakeholders in the research process to provide different perspectives and avoid personal biases. It is important to follow up on bad or unwanted insights with opportunities for improvement and user context information to provide a more holistic view for your audience.
Conclusion
As a UX researcher, it is important to be aware of cognitive biases that may influence your research process and results. Confirmation bias, availability bias, anchoring bias, hindsight bias, and self-serving bias are just a few examples of the many cognitive biases that can impact UX research.
At Key Lime Interactive, our researchers are knowledgeable about these biases and take proactive measure to seek to migrate them. We collect and analyze all relevant data sources and involve a diverse group of participants and stakeholders to mitigate the impact of cognitive bias on their research process. Our approach helps ensure that they deliver impactful results for our clients.
Resources:
Book: Thinking, Fast and Slow by Daniel Kahnman
https://www.playbookux.com/types-of-user-research-bias-and-how-to-avoid-it-in-your-ux-design/
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