cognitive biases word cloud
When conducting user research and testings, are you aware that cognitive biases can occur to both ourselves and the users? These biases threaten the validity of the research, making research insights less applicable.

A cognitive bias refers to a systematic illogical thinking pattern that affects judgments and decisions. These biases allow us to make decisions quicker and easier, but sometimes it also hinders us from generating accurate judgment.

In this article, I have listed out 6 common biases that you may encounter during user research and approaches you can take to avoid them.

Your biases during UX research

1 - Confirmation Bias

Definition: The tendency to search for or only pay attention to information that aligns with your belief.

Example: You are conducting a usability test to test out a UX pattern you designed. You have high confidence that the pattern would work. When synthesizing your test results, you overlook evidence that doesn't support your hypothesis and therefore interpret results inaccurately.

How to overcome it: Remember that goal of user research or usability testing is to learn more about users instead of you being right about users. Pay attention to data points that break your hypothesis and carefully evaluate them.

2 - Observer-Expectancy Effect

Definition: The tendency act out your beliefs and attitudes when conducting your research, which could influence how research participants behave

Example: When conducting a user interview, your tone and body gesture unintentionally pressure the user to answer questions in a certain way.

How to overcome it: Practice your interview ahead of time. Have a colleague/partner provide you feedback on language, tones and non-verbal cues that are not neutral.

User's biases during UX research

3 - Social Desirability Bias

Definition: Your research participants tend to offer responses are that socially desirable instead of their true beliefs

Example: In a focus group, you asked a question that may elicit some controversial opinions. Some participants decided not to reveal their actual thoughts and beliefs due to fear of confrontation and being judged.

How to overcome it: Consider doing one-on-one interviews if the user research involves any controversial topics. Always ensure the confidentiality of responses and communicate that to the participants. Preface the conversation by encouraging your users to openly state their opinion and letting them know there are no right or wrong answers to your question.

4- Hawthorne Effect

Definition: When people are aware that they are being observed, they change their normal behavior unintentionally.

Example: You are observing how a user interacts with an app. The user is informed that his/her actions on the app would be recorded. As a result, the user may be extra careful not to make mistakes on the app to avoid embarrassment.

How to overcome it: Inform the user that there's no right or wrong way of completing their tasks during the research. Provide smaller warm-up tasks at the beginning of the session so that the user can become comfortable with the environment.

Biases in your research design

5- Wording bias

Definition: The bias occurs when a question is framed in a way that suggests an answer

Example: A question in a survey is worded as "How difficult was it for you to set up a doctor's appointment?" The question implies that the process of setting up a doctor's appointment is difficult. It's likely to prompt users only recall negative experience.

How to overcome it: Proofread your questions with a goal of checking whether your questions are neutral. Also, here is a great article on this topic that provides additional insights.

6- Sampling bias

Definition: Some types of users are unintentionally left out from the research participant pool

Example: You are designing a cycling tracking app and need to conduct research on cyclists. You decide to interview and observe cyclists in New York City, but you fail to recognize that their cycling behavior may differ significantly from those who live in suburban areas with plenty of hills. You are running the risk that your research insights may not apply to all your target audience.

How to overcome it: Define your target audience and list out key differentiators in terms of their background, behaviors and attitudes. Include people of different characteristics in your sample.


Cognitive biases occur all the time. As UX practitioners, it is our responsibility to overcome them when they impede our ability to understand users. There are many wonderful resources about cognitive biases.