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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsThe most damaging usability-testing mistakes happen before or during the session: a vague research question, participants unlike real users, tasks that reveal the answer, or a moderator who steps in too quickly. Avoid them by matching the study to a clear product decision, planning for the people and context that matter, observing behavior without steering it, and treating findings as evidence to investigate—not proof about every user.
1. Starting without a focused research question
“Test the app” is not a useful study goal. It invites unrelated tasks and makes it hard to decide which observations matter. Start with the product decision the study should inform, then identify the uncertainties that could change that decision.
- Decision: What will the team decide or change after the study?
- Uncertainty: What do you need to learn to make that decision?
- Evidence: What participant behavior or explanation would help answer the question?
For example, “Can first-time customers find and understand the return process?” is more actionable than “Find usability problems.” The Office for Health Improvement and Disparities’ GOV.UK guidance on qualitative usability testing cautions that adding goals can dilute insight on each one; Digital.gov likewise identifies an overly broad purpose as a weakness in study design.
2. Recruiting people who do not reflect actual users
Convenient participants—colleagues, friends, family, or product experts—may know the service, language, or conventions better than ordinary users. Recruit actual or likely users based on relevant needs, behaviors, goals, and experience. Think about who recruitment channels, session times, location, or technology requirements might exclude.
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For accessibility research, allow time to recruit participants with relevant disabilities or assistive-technology needs. Ask what access arrangements will make participation workable, provide suitable communication support, and avoid repeatedly relying on the same people. GOV.UK’s participant-recruitment guidance discusses recruitment routes and bias; Section 508’s tips for testing with people with disabilities also stress relevant representation and the limits of generalizing from a small number of participants.
3. Treating “five users” as a universal rule
Sample size depends on what the study is meant to establish. A small qualitative study can expose confusing interactions and generate design questions; it does not estimate population-wide success rates. A quantitative benchmark needs more participants and consistent conditions.
| Study purpose | Guidance and context |
|---|---|
| Qualitative usability testing | The Office for Health Improvement and Disparities suggested 5 to 6 participants in its 2020 guidance. Nielsen Norman Group recommends 5 for a traditional qualitative study. These are practical starting points, not guarantees that every issue will be found. |
| Quantitative usability work or eyetracking | Nielsen Norman Group says at least 20–30 participants may be needed in each target user group. The requirement depends on the study and groups being compared. |
| Usability benchmarking | Government Digital Service guidance from 2018 targets 30 to 60 actual or likely users for benchmarking. |
These figures are not interchangeable prescriptions. Distinct user groups may need to be considered separately, and the study’s purpose should determine its design. The Office for Health Improvement and Disparities’ example of 29 participants over four rounds for an EPIC HIV service illustrates iterative refinement and contextual recruitment; it is a case example, not a universal sample-size recommendation. See the qualitative testing guidance, GOV.UK benchmarking guidance, and Nielsen Norman Group’s study-planning checklist.
4. Writing tasks that give away the answer
A task should describe a believable goal, not tell a participant which control or route to use. “Use the filters menu to sort by price” tests whether the participant can follow an instruction; it does not show whether they can find a suitable item on their own.
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- Describe the situation and outcome the participant wants.
- Avoid naming buttons, menus, page names, or the intended sequence of actions.
- Make tasks relevant and challenging enough to reveal friction without adding irrelevant complexity.
- Present one task at a time and use the same neutral wording across sessions.
- Pilot the tasks with someone who can flag ambiguity or accidental clues.
For example, “You need a replacement part that will arrive by Friday. Find an option that meets that need” gives a goal without specifying the route. GOV.UK’s moderated-testing guidance and its benchmarking guidance both emphasize clear, realistic tasks and consistent instructions.
5. Leading participants or helping too much
Participants may worry that they are being tested rather than the service. Explain that the team is evaluating the product, not their ability, then give them room to attempt each task. If they get stuck, resist pointing to a control or suggesting the next step; that can hide the very problem the study is meant to reveal.
Use open, neutral follow-ups tied to what you observed: “What were you looking for there?” or “What did you expect to happen?” Avoid questions that imply a preferred answer, such as “Wasn’t that button easy to find?” A note-taker can capture behavior and context while the facilitator stays focused on the participant.
The Office for Health Improvement and Disparities’ qualitative testing guidance advises giving a task and letting the participant complete it rather than influencing how they engage with the prototype. Digital.gov’s usability-testing guide also recommends neutral questions and alignment between study purpose and tasks.
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6. Choosing a setting that hides important context
A lab or remote call is not automatically the right environment. If the task depends on a participant’s device, surroundings, connectivity, or configured assistive technology, a generic setup may conceal barriers. Where those factors matter, consider observing the person in their usual context or letting them use their own device and tools.
In-person sessions can reveal subtle cues and context; remote sessions can make participation more accessible or help reach people who are difficult to schedule, but it may be harder to guide participants or interpret interaction. Moderated sessions allow clarification and probing. Unmoderated sessions can be quicker and cheaper, but offer less opportunity to understand why a participant acted a certain way. Choose by research question, participant needs, and the evidence required—not by a universal preference. GOV.UK discusses assistive-technology setups and session formats in its moderated usability-testing guidance; Nielsen Norman Group compares formats in its planning checklist.
7. Treating accessibility as an afterthought
If people with particular access needs are part of the target audience, include them in recruitment and plan the session around how they use the service. Provide enough time to arrange access requirements, communication support, and relevant devices or assistive technology. Do not assume a lab can reproduce a person’s configured setup.
One participant’s experience can identify a barrier, but it cannot stand in for an entire disability group. Usability sessions add evidence about real interaction; they do not replace evaluating conformance against applicable accessibility standards. GOV.UK’s recruitment guidance and Section 508’s disability-testing tips address recruitment and appropriate interpretation.
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8. Overloading sessions or measuring the wrong thing
Too many tasks create fatigue and rushed observations. For benchmarking, Government Digital Service guidance suggests no more than five tasks per participant and up to 10 minutes per task as a rule of thumb. Treat those limits as planning guidance, not a universal threshold; task complexity and session purpose still matter.
For each task, decide in advance what counts as success and record more than elapsed time. Useful observations include:
- Whether the participant completed the task and how.
- Time taken, when timing is relevant to the study question.
- Abandonment, repeated errors, or points of hesitation.
- Whether the participant believed they had succeeded when the outcome showed otherwise.
- Context that helps explain the behavior, not just the final result.
In qualitative research, small samples can reveal patterns and design issues, but reporting percentages or averages without qualification can imply precision the study does not support. For benchmark procedures and measures, see the GOV.UK benchmarking guidance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. Recording without consent or treating observations as proof
Explain whether a session will be recorded, why, who will use the recording, and how personal information will be protected. Obtain informed consent before recording. Use real user data only when the service can handle it securely; otherwise, create realistic dummy data that supports the task without exposing personal information.
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A recording captures a session, not the whole population’s experience. Interpret it alongside observed task outcomes, participant comments, and relevant analytics, and document limitations such as recruitment, setting, or a small sample. GOV.UK’s moderated-testing guidance covers personal data and session conduct; its benchmarking guidance discusses analysis and iteration.
10. Stopping at findings instead of changing and retesting
After sessions, group recurring failures, common errors, and moments where participants’ expectations conflict with the interface. Share the evidence with the team and translate problems into design opportunities. Prioritize changes by the severity and recurrence of observed barriers, then test meaningful changes again.
For benchmarks, keep tasks and conditions sufficiently consistent between rounds to make comparisons useful. Revisit them when the service or user behavior changes enough that the old measure no longer represents the experience you need to understand.
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