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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 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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“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]
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“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]
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“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]
In the citing paper
“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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Huang, K., Cui, Y., Ye, Q., Zhao, Y. L., Zhao, X., Tian, Y., Zheng, K., Hu, H., & Zhou, X. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3312566 · Cited by 2
Gheidar, Y., Lessard, L., & Yao, Y. (2023) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew57809.2023.00084 · Cited by 2
James, M. E., Cairney, J., Odorico, N., Finlay-Watson, T., & Arbour‐Nicitopoulos, K. P. (2023) · Journal of Motor Learning and Development · doi:10.1123/jmld.2023-0016 · Cited by 2
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Metadata
- 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.
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