> ## Content Index > Fetch the complete content index at: https://www.elisatrippetti.com/llms.txt > Use this file to discover other available public pages before exploring further. # Looking out for biases in usability testing - URL: https://www.elisatrippetti.com/bias-usability-testing/ - Published: 2023-02-20T11:00:00.000Z - Updated: 2026-05-19T19:09:27.000Z - Description: Reflections on researcher and participant blind spots for more reliable findings - Author: Elisa Trippetti - Tags: UX writing, User research While talking to recruiters, I've noticed that more and more product teams are looking for UX writers who can help **prepare test scripts** for usability testing. Since this requirement might become more common in the future, I thought it would be helpful to summarize some of the **biases** we need to look out for when conducting usability tests. --- ## Researcher bias Let's focus on researcher bias and see possible ways to avoid it. **Confirmation bias**: the researcher uses answers and insights from the test to confirm their pre-determined hypothesis. **Culture bias**: the researcher interprets test results based on their background, expectations, values and preferences. **Wording bias**: the researcher frames questions in a way that prompts participants to give a specific response. For example, leading questions could be inadvertently used to guide users towards the desired answer. Here are some **tips** to prevent these biases from influencing the test results: - Know what biases **exist**. Note down your assumptions regarding the test and its results. - Ask **neutral, open-ended** questions. Be clear and use simple words. Pay attention to terms and structures that might suggest the answer. For example, ask *"What was the task like?"* instead of *"How DIFFICULT was the task?"* - Involve colleagues from **varied backgrounds** and who are familiar with different research methods. They can help you spot inconsistencies and issues you might have missed. - As an additional data point, conduct competitor audits and find out what users like and dislike about similar brands. --- ## Participant bias Users become more **self-aware** when they know they're being watched, and might adjust their behavior accordingly ([Hawthorne effect](https://catalogofbias.org/biases/hawthorne-effect/?ref=elisatrippetti.com)). *"It feels like these researchers are observing and analyzing my every move. I'll need to do my best for the test to succeed.* Users know that, if the researcher asks them to complete a certain action, it means that it's in fact **possible** to complete it. This might affect the way they approach the task at hand. *"Since you've asked me to do it… it means it can be done."* When users perceive a question **matters** to the researcher, they'll try to give them an opinion anyway (even if they don't know too much about the topic). *"Since you've asked me to do it… it must be important."* Instead of giving a genuine answer, users will say what they think is considered the 'right' answer ([social desirability](https://catalogofbias.org/biases/unacceptability-bias/?ref=elisatrippetti.com)). *"I'll tell you what I think you want to hear."* Some ways to **avoid** participant bias include: - Clarifying that there are **no right or wrong** answers. What the researcher cares about is understanding what users actually think and feel. - Never showing frustration or disapproval toward users and their answers. The researcher wants to be friendly and create a **safe environment** where participants feel they can be truthful. There will be no repercussions if they share negative feedback on our product. I think it's also important to understand where **user nervousness** may come from. - Is it because they're being observed? - Because they're unfamiliar with the product? - Because they're uncomfortable providing negative feedback? As we keep these aspects in mind, we need to try and address questions and objections upfront, while creating a safe space for users to be honest. No judgment, no negative repercussions (regardless of the feedback) and no disappointed reactions to the participants' answers. We won't always get a chance to talk to all participants directly to reassure them, but we can anticipate some of the hiccups by learning about the researcher's own biases. What I've found is that, once I've narrowed down my participant list to the most relevant candidates, I'm usually left with people who are passionate about the product and actively use it. They take the test very seriously and usually point out interesting aspects I hadn't thought about in the first place. Maybe these same users would like to be involved in future studies or receive some kind of benefit from the brand. They don't want to hurt their chances by sharing negative feedback. Setting the tone upfront really helps when it comes to minimizing these biases. [![CTA Image](https://storage.ghost.io/c/dc/8f/dc8fe240-9762-45ca-b607-5f39c80e84a5/content/images/2026/05/Elisa-Trippetti-3.jpg)](https://www.elisatrippetti.com/) ****Read more from me** I'm Elisa, an Italian content designer with a background in localization and customer service. This is where I document my life in UX and writing. [Go to the blog ](https://www.elisatrippetti.com/) ## Let’s talk words Get in touch on [LinkedIn](https://www.linkedin.com/in/elisatrippetti/?ref=elisatrippetti.com) to talk about all things content design, UX writing and localization. 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