Beyond the five-user assumption: benefits of increased sample sizes in usability testing
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It is widely assumed that 5 participants suffice for usability testing. In this study, 60 users were tested and random sets of 5 or more were sampled from the whole, to demonstrate the risks of using only 5 participants and the benefits of using more. Some of the randomly selected sets of 5 participants found 99% of the problems; other sets found only 55%. With 10 users, the lowest percentage of problems revealed by any one set was increased to 80%, and with 20 users, to 95%.
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Purposive sampling was used to select 10 individuals with at least 50% of specified characteristics.
“Purposive sampling was used to select 10 individuals 43 with at least 50% of the following characteristics: 65þ years old, individuals of diverse race/ethnicity, male, education less than college, and taking at least 3 medications.”
From A chatbot for hypertension self-management support: user-centered design, development, and usability testing (2023) · cites this paper as [43]
The Results and Analyses section reports findings for groups of 10 usability-test users, so the paper supports using 10 as a sample size. Its Method section describes 60 participants grouped by experience, not purposive selection of 10 people meeting the stated age, demographic, education, and medication criteria or a 50% threshold. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPrior research suggests that the sample size is adequate for usability testing.
“Prior research suggests that our sample size is adequate for usability testing, 43”
From A chatbot for hypertension self-management support: user-centered design, development, and usability testing (2023) · cites this paper as [43]
The Conclusion (page not available) reports that increasing a usability test from 5 to 10 or 20 users improves confidence, but says the number required depends on the users and the system. Because the citing sentence does not give its sample size or testing conditions, this paper cannot establish that its particular sample was adequate. (LLM, conf=0.78)
Experimental — accuracy not yet measuredTen participants were chosen at each stage of the recruitment process because the mean percentage of problems identified in usability studies rises from 85% to 95% when the number of participants increases from 5 to 10.
“Ten participants were chosen at each stage of the recruitment process—the mean percentage of problems identified in usability studies rises from 85% to 95% when the number of participants increases from 5 to 10 [36].”
From A user-centred design framework for mHealth (2020) · cites this paper as [36]
The sample size of 10 was chosen in line with prior work around formative testing for usability, aiming to explore first, easy-to-find problems users encounter.
“Our sample size was chosen in line with prior work around formative testing for usability [19,43]: our goal is to explore the frst,”
From Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts (2023) · cites this paper as [19]
Simulations suggest that 10 users will identify a minimum of 80% of the problems with the technology during usability testing, and 20 users will identify 95% of the problems.
“This may be reasonable, given that simulations suggest that 10 users will identify a minimum of 80 % of the problems with the technology during usability testing, and 20 users will identify 95 % of the problems [42].”
From Maximising the impact of qualitative research in feasibility studies for randomised controlled trials: guidance for researchers (2015) · cites this paper as [42]
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Experience with usability evaluation of e-learning systems
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Empowering digital twins with eXtended reality collaborations
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- Authors
- Faulkner, L.
- Affiliations
- Applied Research Laboratories, The University of Texas at Austin (US)
- Corresponding author
- Faulkner, Laura
- Venue
- Behavior Research Methods, Instruments, & Computers
- Published
- 2003-08
- Volume
- 35
- Issue
- 3
- Pages
- 379-383
- ISSN
- 0743-3808 (ISSN-L)
- 1532-5970
- OpenAlex
- W2088028234
- Semantic Scholar
- 7369703e67a6812b4476fc95f8085c4d20ebe2ff
- Cited by
- 1,053
- References
- 9
- Citations by year
- 2026: 95 · 2025: 83 · 2024: 98 · 2023: 83 · 2022: 83 · 2021: 92 · 2020: 81 · 2019: 59 · 2018: 61 · 2017: 51 · 2016: 46 · 2015: 39 · 2014: 33 · 2013: 33 · 2012: 25
- FWCI
- 8.34
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Behavior Research Methods, Instruments, & Computers (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Computer Science › Human-Computer Interaction › Persona Design and Applications
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- usability testing, sample size, number of participants, random sampling, problem identification
- MeSH terms
- Consumer Behavior, Data Collection, Humans, Product Surveillance, Postmarketing, Software, Task Performance and Analysis, User-Computer Interface, Sample Size, Internet
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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