Observer bias is a type of experimenter bias that occurs when a researcher’s expectations, perspectives, opinions, or prejudices impact the results of an experiment. This type of research bias is also called detection bias or ascertainment bias.
This typically occurs when a researcher is aware of the purpose and hypotheses of a study and holds expectations about what will happen.
Key Takeaways
- Definition: Observer bias happens when a researcher’s expectations shape what they see or record, distorting the data without necessarily being deliberate.
- Impact: In clinical trials, non-blinded assessors rated the same outcomes far more favourably than blinded assessors, inflating treatment effects by roughly two-thirds on average.
- Risk Factors: Subjective, judgement-based measures are more vulnerable than objective, instrument-based ones, though neither is fully immune.
- Prevention: Blinding, standardized coding manuals, trained and multiple observers, and random assignment all reduce the risk.
- Related Biases: Observer bias is distinct from the observer-expectancy effect, actor-observer bias, and the Hawthorne effect, though all can distort a study’s results.
- Modern Evidence: A 2026 meta-analysis of 28 studies found only about 15% of participants change behaviour because of an unobtrusive camera, well below the rate for a visible human observer.
A researcher may be searching for a particular result to support the hypothesis, or may already hold a predetermined idea of what the results should be. Either way, the temptation is real. They may twist the data to better fit their predictions.
This bias occurs most often in observational studies or any type of research where measurements are taken and recorded manually.
In observational studies, a researcher records behaviors or takes measurements from participants without trying to influence the outcome of the experiment.
Observational studies are used in a number of different research fields, most specifically medicine, psychology, behavioral science, and ethnography.
For Example
You are performing an observational study to investigate the effects of a new medication to treat nausea. Group A receives the actual treatment with the new medication, while group B receives a placebo.
The participants do not know which group they are a part of, but you – the researcher – do.
Unconsciously, you treat the two groups differently, framing questions more negatively towards Group B and commenting that those in Group A seem more energized and upbeat.
Impact of Observer Bias
Observer bias can result in misleading and unreliable results. A researcher’s biases and prejudices can affect data collection and observer interpretation, leading to results that fail to represent accurately what exists in reality.
It might also result in inaccurate data sets, misleading information, or biased treatment from researchers.
Observer bias can damage scientific research and policy decisions and lead to negative outcomes for people involved in the research studies.
A large-scale systematic review put a number on that damage.
Aim: to establish how large observer bias actually is in clinical trials with subjective, scale-based outcomes, rather than only asking whether blinding is recommended in principle.
Method: a systematic review identified trials where the same outcome, on the same patients, had been rated by both a blinded and a non-blinded assessor. A meta-analysis pooled the difference between the two ratings across 16 such trials covering 2,854 patients.
The numbers tell the story.
Results: the gap was large. Non-blinded assessors gave systematically more favourable ratings than blinded assessors scoring the identical outcome, inflating the pooled treatment effect by roughly 68% on average (Hróbjartsson et al., 2013).
Conclusion: failing to blind an observer to condition is not a minor methodological detail. It produces a high risk of substantial bias in a study’s reported results (Hróbjartsson et al., 2013).
Why Observer Bias Can Happen
Subjective Methods
Subjective research methods are those that involve some type of interpretation before you record the observations. Subjectivity refers to the way research is influenced by the perspectives, values, emotions, social experiences, and viewpoints of the researcher.
This could lead a researcher to record some observations as relevant while ignoring other equally important observations.
Even if a researcher is subconsciously primed to see only what they expect to observe, subjective research methods could lead to skewed conclusions.
Objective Methods
Although objective research tends to be impartial and fact-based, observer bias might still influence studies that use more objective methods.
This is because researchers tend to interpret or record readings differently, skewing the results to be more in line with their predictions.
For example, when measuring blood pressure using a blood pressure monitor, a researcher might round up the blood pressure to the nearest whole number.
Or, due to familiarity with the procedures of measuring blood pressure, a researcher might be less careful when taking the measurements and thus record inaccurate results.
How to Minimize Observer Bias
Blinding
Blinding, or masking, ensures that the participants and researchers are all unsure of the goals of the study.
This will help eliminate some of the research expectations that come from knowing the study’s purpose so observers are less likely to be biased.
Additionally, in double-blind studies, neither the researchers nor the subjects know which treatments are being used or which group they belong to.
Random Assignment
Randomly assigning subjects to groups instead of choosing the subjects themselves will help minimize observer bias.
Multiple Observers
Having multiple researchers involved in the research study will ensure that your data is consistent and make it less likely that one researcher’s biases will significantly affect the project’s outcome.
It can also be beneficial to use multiple data collection methods for the same observations to corroborate your findings and check that they line up with each other.
This works because it lets researchers check inter-observer reliability. It asks whether independent observers scoring the same behaviour agree. Cohen’s kappa and the intraclass correlation coefficient are the usual measures.
Good agreement matters. It is what lets a reader trust the coded behaviour, not just one person’s judgement.
This safeguard is recommended more often than it is used. A 2012 review of five major behavioural-research journals found that most published studies did not report blind coding or a formal inter-observer reliability check (Burghardt et al., 2012).
A follow-up study repeated the same audit on those journals’ 2020 volumes. Reporting rates had risen, sometimes substantially, but still lagged behind comparable developmental research (Freeberg et al., 2024).
Train Observers
Before beginning a study, it is beneficial to train all observers in the procedures to ensure everyone collects and records data exactly the same way.
This will eliminate any variation in how different observers report the same observation, keeping interrater reliability high and minimizing observer bias.
The most effective training tool is a detailed coding manual. It defines exactly what each behaviour category means, with concrete examples, so every observer applies it the same way.
Training also needs to continue after a study begins. Coders can drift, gradually shifting how they apply a category over a long study, usually because the original definition was not tight enough. Periodic recalibration against reference recordings catches this drift before it distorts the data.
Standardized Procedures
It is important to create standardized procedures or protocols that are easy for all observers to follow.
You can record these procedures so that the researchers can refer back to them at any point in the research process.
Good standardized procedures rest on a clear operational definition. That means a precise, measurable specification of what counts as an instance of the target behaviour. Without one, two observers can watch the same moment and record something different simply because they are using the term differently.
Low-inference categories help too. A code like “eyes closed” leaves little room for interpretation. A code like “showing empathy” leaves much more, and is harder for different observers to apply consistently.
Related Biases
Observer bias is closely related to several other types of research bias.
Observer-Expectancy Effect
The observer-expectancy effect occurs when a researcher’s cognitive bias causes them to subconsciously influence the results of their own study through interactions with participants.
Researchers might unconsciously or deliberately treat certain subjects differently, leading to unequal results between groups.
For example, a researcher might ask different questions or give different directions to one group but not another. Body language matters too. A researcher might unconsciously change their posture, tone of voice, or appearance around certain participants.
Actor-Observer Bias
Actor-observer bias is an attributional bias where a researcher attributes their own actions to external factors while attributing other people’s behaviors to internal causes.
This bias can help explain why we are inclined to blame others for things that happen, even when we would not blame ourselves for acting in the same way.
For example, if you perform poorly on a test, you might blame the result on external factors such as teacher bias or the questions being harder than usual.
However, if a classmate fails the same test, you might attribute their failure to a lack of intelligence or preparation.
Hawthorne Effect
The Hawthorne effect refers to some participants’ tendency to work harder and perform better when they know they are being observed.
This effect also suggests that individuals may change their behavior due to the attention they are receiving from researchers rather than because of any manipulation of independent variables.
How big is this effect in practice? A 2026 systematic review and meta-analysis pooling 28 studies found that only around 15% of participants reported changing their behaviour because of an unobtrusive video camera.
That is well under half the 42.2% rate reported for a directly present human observer in the same studies. A camera left running is a real but modest reactivity risk, and a person standing in the room is a bigger one (Herath et al., 2026).
Experimenter Bias
Experimenter bias is any type of cognitive bias that occurs when experimenters allow their expectations to affect their interpretation of observations.
Experimenter bias typically refers to all types of biases from researchers that might influence a study, including observer bias, the observer-expectancy effect, actor-observer bias, and the Hawthorne effect.
When a researcher has a predetermined idea of the results of their study, they might conduct the study or record results in a way that confirms their theory.
FAQs
What is the difference between observer bias and confirmation bias?
Observer bias is a type of experimenter bias where a researcher’s predetermined expectations, perspectives, opinions, or prejudices can impact the results of an experiment.
Confirmation bias is a type of cognitive bias that occurs when a researcher favors information or interprets findings to favor their existing beliefs.
Unlike observer bias which can be intentional in some instances, confirmation bias happens due to the natural way our brains work, so it cannot be eliminated.
What is the difference between observer bias and the observer effect?
The observer effect in psychology is also known as the Hawthorne effect. It refers to how people change their behavior when they know they are being observed in a study.
Observer bias is a related term in the social sciences. It refers to the error that results from an observer’s cognitive biases. Specifically, observers overemphasize behavior they expect to find and fail to notice behavior they do not expect.
The observer effect is not to be confused with the observer-expectancy effect or the actor-observer bias, discussed above.
Further Reading
Burghardt, G. M., Bartmess‐LeVasseur, J. N., Browning, S. A., Morrison, K. E., Stec, C. L., Zachau, C. E., & Freeberg, T. M. (2012). Perspectives–minimizing observer bias in behavioral studies: a review and recommendations. Ethology, 118(6), 511-517.
Freeberg, T. M., Benson, S. A., & Burghardt, G. M. (2024). Minimizing observer bias in animal behavior studies revisited: Improvement, but a long way to go. Ethology, 130(6).
Herath, M., Kulas, S., Martin, J., Treloar, E. C., Ey, J. D., Bradshaw, E. L., DeSilva-White, J., Edwards, S., Bruening, M., & Maddern, G. J. (2026). Evaluating the impact of video cameras on participant behaviour in research: A systematic review and meta-analysis. Systematic Reviews, 15, Article 65.
Hróbjartsson, A., Thomsen, A. S. S., Emanuelsson, F., Tendal, B., Hilden, J., Boutron, I., … & Brorson, S. (2013). Observer bias in randomized clinical trials with measurement scale outcomes: a systematic review of trials with both blinded and nonblinded assessors. Cmaj, 185(4), E201-E211.
Salvia, J. A., & Meisel, C. J. (1980). Observer bias: A methodological consideration in special education research. The Journal of Special Education, 14(2), 261-270.
