Single-blind A/B Test — Controlled Experiment with Participant Blinding
Single-blind A/B Test · Also known as: single-masked A/B test, single-blind split test, blinded two-condition experiment, participant-blind A/B test
A single-blind A/B test is a controlled two-condition experiment in which participants are randomised to condition A (control) or condition B (treatment) but are kept unaware of which condition they have received, while researchers and analysts remain aware. The blind prevents participants from changing their behaviour in response to knowledge of their assignment, reducing demand characteristics and response bias while still allowing the investigator to monitor the trial.
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When to use it
Use a single-blind A/B test when participant awareness of condition assignment would distort outcomes (demand characteristics, expectation effects, or social desirability bias) but where researcher-side blinding is impractical or unnecessary. It is well-suited to UX experiments, product feature tests, online platform experiments, and educational interventions where the researcher must monitor the trial actively. Do not use it when observer bias on the researcher's side is a plausible threat — in that case upgrade to a double-blind design. Also avoid it when the intervention is impossible to conceal (e.g., a surgical procedure the patient is awake for) or when deception raises insurmountable ethical concerns.
Strengths & limitations
- Reduces participant-side demand characteristics and expectation effects that inflate or deflate treatment estimates.
- Simpler to implement than double-blind designs because researchers retain full operational awareness of assignment.
- Causal inference is valid when randomisation is correctly executed — the blinded two-group structure directly supports treatment effect estimation.
- Broad applicability: appropriate in clinical, UX, educational, and online platform contexts.
- Allows active monitoring and adaptive stopping by researchers who know which group is which.
- Does not protect against researcher or analyst bias — knowledge of assignment on the research side can influence assessment, data collection decisions, or interim analysis.
- Blinding participants is sometimes impossible or ethically problematic, limiting the design to contexts where neutral presentation is feasible.
- Valid for exactly two conditions; extensions to three or more arms require a multi-arm design.
- Provides no information about interactions between factors; a factorial design is needed when two or more factors are of interest.
Frequently asked
What is the difference between a single-blind and a double-blind A/B test?
In a single-blind A/B test, only participants are unaware of their condition assignment; researchers and analysts know which group each participant belongs to. In a double-blind design, both participants and those who interact with or assess them are kept unaware. Single-blind controls participant expectation effects; double-blind additionally controls researcher and assessor bias. Choose double-blind when the researcher's knowledge could distort outcome measurement.
How do I know if the blinding has actually worked?
After the experiment, ask participants which condition they believed they were in. If participants guess at rates significantly above chance, blinding has likely failed. Pre-designing stimuli to be visually or experientially indistinguishable between conditions is the most reliable preventive measure.
How large does my sample need to be?
Sample size depends on the minimum detectable effect you care about, the desired statistical power (conventionally 80% or 90%), and your significance threshold (conventionally 0.05). Run a power calculation before data collection. For conversion rates in online experiments, minimum samples are often in the hundreds to thousands; for clinical outcomes, formal power analysis using pilot data is required.
Can I run a single-blind A/B test without deceiving participants?
Often yes. In many contexts — such as UI experiments — participants are simply not told which variant they see, which is not deception in the ethically significant sense. When the study involves health interventions or explicit concealment of information that participants would reasonably expect to know, institutional review board guidance on deception and debriefing applies.
What statistical test should I use to compare the two groups?
For continuous normally distributed outcomes, use a two-sample t-test. For binary outcomes (conversion, click), use a chi-square test or a z-test for proportions. For non-normal continuous data, use the Mann-Whitney U test. Always pre-specify the test and whether it is one-tailed or two-tailed before looking at results.
Sources
- Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265
- Schulz, K. F., & Grimes, D. A. (2002). Blinding in randomised trials: hiding who got what. The Lancet, 359(9307), 696–700. DOI: 10.1016/S0140-6736(02)07816-9 ↗
How to cite this page
ScholarGate. (2026, June 3). Single-blind A/B Test. ScholarGate. https://scholargate.app/en/experimental-design/single-blind-ab-test
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- AB DesignExperimental design↔ compare
- Adaptive A/B testExperimental design↔ compare
- Blocked A/B TestExperimental design↔ compare
- Double-blind A/B testExperimental design↔ compare
- Multi-arm experimentExperimental design↔ compare
- Randomized Controlled TrialExperimental design↔ compare