Most common types of bias in research include selection bias, recall bias, observer bias, response bias, attrition bias, confirmation bias, publication bias, performance bias, measurement bias and funding bias.
Types of bias in research can affect every stage of a study, from participant selection to data interpretation and publication. These biases compromise validity, distort evidence and reduce credibility. Understanding the most common types of bias is essential for designing rigorous, trustworthy research.
This post explores the most frequent types of bias in research, detailing how they occur, what consequences they carry and how to reduce them. Moreover, it explains how bias can enter different academic texts, including journal articles, case studies and qualitative research. The article also highlights how professional academic services like line editing, copyediting and proofreading help mitigate these biases before publication.
List of contents
- Selection bias
- Confirmation bias
- Publication bias
- Recall bias
- Observer bias
- Response bias
- Attrition bias
- Funding bias
- Performance bias
- Measurement bias
- Information bias
- Researcher bias
- Cognitive bias
- How to avoid bias in research?
- Bias in research texts
- Editing services
- Resources
Key takeaways
- Types of bias in research include selection bias, confirmation bias, publication bias, observer bias, response bias and others.
- Each bias affects research differently — some distort sampling, others influence interpretation or visibility of results.
- Mitigation strategies include random sampling, blinding, preregistration, transparent reporting and neutral survey design.
- Bias can appear across academic texts, including research articles, literature reviews, theoretical papers and policy documents.
Selection bias
Selection bias occurs when the process of recruiting participants leads to a sample that does not represent the target population. This bias compromises the external validity of the research.
Alternative terms: ascertainment bias
Subtypes:
- Attrition bias: Systematic differences between groups in withdrawals from a study.
- Non-response bias: Differences between those who respond to a survey and those who do not.
- Sampling bias: Certain members of the population are more likely to be included than others.
- Self-selection bias: Individuals volunteer to participate, potentially differing from those who do not.
- Survivorship bias: Focusing only on subjects that passed a selection process, overlooking those that did not.
- Undercoverage bias: Some members of the population are inadequately represented.
How it happens: Researchers may unintentionally select participants who are healthier, more motivated or more accessible than others.
Example: A clinical trial on diabetes includes only patients from a private clinic. These participants may have better healthcare access than the general diabetic population, which limits the study’s generalisability.
How to mitigate it: Use random sampling methods and ensure the sample reflects the target population. Clearly define inclusion and exclusion criteria and avoid convenience sampling.
Confirmation bias
Confirmation bias affects how researchers interpret evidence. It involves focusing on data that supports the hypothesis while overlooking results that contradict it.
Alternative term: myside bias
How it happens: A researcher expects a drug to reduce anxiety. During analysis, they emphasise a small improvement in one symptom while ignoring several areas where the drug showed no effect.
Example: A psychology study on mindfulness training finds no overall improvement in stress levels but highlights a small, statistically significant improvement in sleep, framing the intervention as effective.
How to mitigate it: Pre-register hypotheses and analysis plans. Use blinded data analysis when possible, and involve independent reviewers to interpret results objectively.
Publication bias
One of types of bias is publication bias, which affects the visibility of research findings. Journals are more likely to accept studies that report positive or statistically significant results, creating a distorted evidence base.
How it happens: Researchers and journals may view null results as less valuable. As a result, these studies remain unpublished.
Example: Out of ten trials on a new therapy, only the three with positive outcomes appear in peer-reviewed journals. Systematic reviews based on published data may then overstate the therapy’s effectiveness.
How to mitigate it: Submit all findings for publication, including null or negative results. Register trials and studies in public databases to ensure transparency and traceability.
Recall bias
Recall bias arises when participants cannot accurately remember past behaviours or events. This type of bias frequently affects retrospective studies.
How it happens: Participants may reconstruct memories based on current experiences or outcomes.
Example: A study asks adults to recall their childhood exposure to second-hand smoke. Those with respiratory illnesses may recall such exposure more readily than healthy participants, even if the actual exposure was similar.
How to mitigate it: Use objective data sources, such as medical records or wearable devices, instead of relying on memory. Keep recall periods short to reduce inaccuracy.
Observer bias
Observer bias occurs when researchers’ expectations influence how they record or interpret data. This bias is particularly problematic in studies that are not double-blind.
Related effects:
- Pygmalion effect / Rosenthal effect: Higher expectations lead to better participant performance due to unconscious cues or support from researchers.
How it happens: Knowledge of group assignment can influence the observer’s perception or rating of outcomes.
Example: In a trial for a new pain medication, a doctor knows which patients receive the drug. They might unconsciously record greater pain relief in that group, even if patients report similar levels of discomfort.
How to mitigate it: Apply double-blind designs where both researchers and participants are unaware of group assignments. Standardise measurement procedures to ensure consistency.
Response bias
Response bias is one of most common types of bias. It affects the accuracy of survey or interview data. It occurs when participants give inaccurate answers due to social pressure, question phrasing or personal expectations.
Subtypes:
- Acquiescence bias: Participants tend to agree with statements, regardless of content
- Courtesy bias: Participants provide answers they think will please the interviewer.
- Demand characteristics: Participants alter behavior based on their perceptions of the study’s purpose.
- Extreme responding: Participants choose the most extreme response options regardless of nuance
- Question-order bias: Earlier questions influence responses to subsequent ones.
- Social desirability bias: Participants answer in a way that reflects well on them
How it happens: When participants give inaccurate or misleading answers, often because of question wording, social pressure, interviewer influence or their desire to appear in a certain way.
Example: A survey on sexual health asks about condom use. Respondents may overstate their use to appear responsible, resulting in misleading conclusions.
How to mitigate it: Design neutral, clear and anonymous surveys. Avoid leading questions and provide balanced response options to reduce pressure or guesswork.
Attrition bias
Attrition bias results from unequal dropout rates across groups during a longitudinal study. If dropouts differ significantly from those who remain, the final sample may no longer be representative.
Alternative term: loss to follow-up bias
How it happens: Participants who find the study too demanding or receive no benefits may drop out.
Example: In a year-long fitness programme trial, those who fail to lose weight may quit early. The remaining sample consists mostly of successful participants, exaggerating the programme’s benefits.
How to mitigate it: Use intention-to-treat analysis and track reasons for dropout. Encourage participant retention through regular follow-ups and support during long studies.
Funding bias
Funding bias arises when financial sponsors influence the study design, interpretation or reporting in a way that benefits their interests. This bias often appears in industry-funded research.
How it happens: Sponsors may pressure researchers to use specific endpoints, exclude certain results or delay publication of unfavourable findings.
Example: A pharmaceutical company funds a trial on its own cholesterol drug. The published report highlights secondary benefits but downplays serious side effects that emerged during the trial.
How to mitigate it: Disclose all funding sources. Maintain independence in study design, data analysis and reporting, even when industry partners are involved.
Performance bias
Performance bias occurs when participants or personnel behave differently across study groups due to knowledge of group assignment. This typically affects studies that lack blinding.
Related effects:
- Hawthorne effect: Participants improve performance because they know they are being observed.
- John Henry effect: Control group participants exert extra effort to compensate for not receiving the intervention.
How it happens: Participants who know they are receiving treatment may engage more with the intervention. Staff may also treat them more attentively.
Example: In an exercise intervention study, instructors encourage participants in the experimental group more than those in the control group. This additional motivation, not the intervention, influences outcomes.
How to mitigate it: Use blinding and standardised protocols for all groups. Ensure all participants receive equal attention and support throughout the study.
Measurement bias
Measurement bias refers to systematic errors in how data are collected. It can occur due to faulty instruments, inconsistent procedures or poor calibration.
Alternative term: information bias
Related effect:
- Halo effect: A positive impression of a participant (e.g. appearance or charisma) influences how researchers rate their performance.
How it happens: A researcher uses a device that overestimates blood pressure. If this device is used throughout a study, the findings will reflect inflated values.
Example: A depression survey contains poorly worded questions that confuse participants. Their answers may not accurately reflect their mental health status, leading to misleading conclusions.
How to mitigate it: Calibrate tools regularly and use validated measurement instruments. Train data collectors to apply methods consistently across participants.
Information bias
Information bias is a type of bias in research that occurs when collected data are inaccurate or systematically misclassified due to measurement errors or inconsistent data collection methods.
Subtypes:
- Observer bias: Researchers’ expectations influence their observations or recordings.
- Performance bias: Differences in care provided apart from the intervention being evaluated.
- Recall bias: Differences in accuracy or completeness of participant recollections.
- Regression to the mean: Extreme values tend to move toward the average on subsequent measurements.
How it happens: When data are collected, recorded or measured inaccurately or inconsistently, often due to faulty instruments, misclassification, recall errors or differences in how information is obtained across groups.
Example: Patients inaccurately recalling their dietary habits when reporting to researchers.
How to mitigate it: Validate measurement tools, apply standardised data collection procedures, train data collectors consistently and, when possible, rely on objective records rather than self-reported data.
Researcher bias
Researcher bias occurs when a researcher’s expectations or preferences influence the study’s outcome.
How it happens: A researcher’s beliefs, expectations or preferences influence the design, data collection, analysis or interpretation of a study, either consciously or unconsciously, leading to skewed or selective results.
Example: A researcher interprets ambiguous data in a way that supports their hypothesis.
How to mitigate it: Use strategies such as pre-registering the study design and analysis plan, applying blinding during data collection and analysis, using standardised protocols and involving independent researchers to review methods and interpret results.
Cognitive bias
Cognitive bias involves systematic patterns of deviation from norm or rationality in judgment, affecting decisions and interpretations.
Common subtypes:
- Anchoring bias: Relying heavily on the first piece of information encountered.
- Availability heuristic: Overestimating the importance of information readily available.
- Confirmation bias: Favoring information that confirms existing beliefs.
- Example: Assuming a well-dressed individual is also intelligent and competent.
- Framing effect: Decisions influenced by the way information is presented.
- Halo effect: Perception of one positive trait influences the perception of other traits.
How it happens: It arises unconsciously during reasoning, especially under pressure, when interpreting data, forming hypotheses or making assumptions.
Example: A researcher shows confirmation bias by focusing only on data that supports their theory while ignoring contradictory results.
How to mitigate it: Use pre-defined research protocols, engage in peer review, seek contradictory evidence, involve diverse team members and apply blinding when interpreting results.
How to avoid bias in research?
To effectively avoid bias in research, researchers must take deliberate steps at every stage of the study — from design and data collection to analysis and publication. Below is an expanded guide on how to reduce the most common types of bias in research:
- Use random sampling: Select participants randomly from the target population to ensure that each individual has an equal chance of being included. This helps prevent selection bias, sampling bias and undercoverage bias, improving the study’s external validity.
- Apply blinding: Blinding minimises observer bias and performance bias by keeping participants, researchers or both unaware of group assignments.
- Single-blind: Participants do not know their group allocation
- Double-blind: Neither participants nor researchers know who is in which group
- Pre-register study protocols: Register hypotheses, methods and analysis plans in a public registry before starting the study. This deters confirmation bias, selective reporting and p-hacking.
- Platforms: Open Science Framework, ClinicalTrials.gov
- Design neutral, anonymous surveys: Carefully word survey and interview questions to avoid response bias, such as social desirability bias, acquiescence bias or courtesy bias.
- Example: Avoid leading questions like ‘Do you agree that…?’ and ensure anonymity to encourage honest responses.
- Ensure transparency and declare conflicts: Disclose funding sources, institutional affiliations and any potential conflicts of interest. This reduces the risk of funding bias and improves research credibility.
- Involve diverse and independent reviewers: Use peer review and collaboration with researchers from varied backgrounds to challenge assumptions and reduce researcher bias and cognitive bias such as the halo effect or anchoring bias.
- Conduct pilot studies: Pilot studies help detect potential biases early by testing methods, instruments and procedures on a small scale before full implementation.
Types of bias in research texts
Academic texts may carry various types of bias, which can affect research quality, interpretation and credibility. These biases often emerge in how researchers collect data, interpret findings or present arguments. Below are common academic texts that may carry types of bias:
- Research articles: Authors may introduce researcher bias, confirmation bias or publication bias by selectively reporting results or interpreting data to support their hypotheses.
- Literature reviews: Bias may occur when authors favour certain sources, exclude contradictory studies or rely heavily on well-known researchers, leading to selection bias or authority bias.
- Case studies: These texts may include observer bias or selection bias if the researcher chooses cases that support a specific narrative.
- Qualitative studies: Interviews and ethnographies may carry interviewer bias or response bias, especially when the researcher influences participants’ answers.
- Experimental studies: Randomisation or blinding errors can lead to performance bias or detection bias, especially in clinical trials.
- Theoretical papers: Authors may show ideological bias or cultural bias if they rely on assumptions from a narrow framework without acknowledging alternatives.
- Policy papers and white papers: These may reflect political or institutional bias depending on funding sources or the authors’ affiliations.
Editing services
Professional academic services can significantly improve the quality of research texts by helping authors identify and reduce types of bias before publication. These services offer different levels of editorial support, each playing a distinct role:
Line editing
Line editors focus on the tone, sentence structure and logical flow of the text. They help authors express complex ideas clearly and improve the rhythm and clarity of argumentation. This level of editing can reveal underlying researcher bias or subjectivity by identifying language that frames findings in an overly persuasive or unbalanced manner. Line editors can also flag inconsistencies between the methods, results and conclusions, which may result from unintentional bias in interpretation. Moreover, they encourage authors to present alternative viewpoints and support claims with stronger evidence, thereby enhancing objectivity.
Copyediting
Copyeditors check for grammar, spelling, punctuation and consistency. More importantly, they ensure clarity and coherence in academic writing. At this stage, copyeditors may flag potential types of bias in language use, such as emotionally loaded terms, vague claims or generalisations that lack evidence. They may also question selective referencing or highlight one-sided interpretations, which could reflect confirmation bias. In addition, copyeditors correct inconsistent terminology and align the manuscript with the required citation style, reducing citation bias and increasing transparency.
Proofreading
Proofreaders perform the final check before submission. While they do not typically address content, they can still spot technical errors that may lead to types of bias. These include mislabelled tables, mismatched figures, or incorrect citations, all of which can mislead readers and introduce information bias. Proofreaders ensure accuracy in formatting, spelling and references, helping the author present a polished, credible manuscript that upholds academic standards.
Resources
- Bad Science by Ben Goldacre explores publication and funding bias in scientific research.
- Everything Hertz podcast covers meta-research, reproducibility and bias in the sciences.
- The Black Goat is a podcast about academic life, including episodes on peer review, preregistration and research transparency.
- The Craft of Research by Wayne C. Booth, Gregory G. Colomb and Joseph M. Williams offers guidance on structuring arguments and avoiding bias in academic writing.
- Thinking, Fast and Slow by Daniel Kahneman explains common cognitive biases and how they affect judgement.
- You Are Not So Smart podcast discusses various forms of cognitive bias and critical thinking.
Conclusion
Bias in research is often subtle but can significantly undermine the credibility and reliability of findings. By recognising the many types of bias that can affect research and applying clear mitigation strategies, researchers can strengthen their work and contribute more transparently to their field.
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