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  • Writer's pictureSourav Dhar

16 Types of Bias to be Careful of in UX Research: Part 2

Updated: Mar 14, 2023


By conducting a systematic enquiry, the researcher may unintentionally or purposefully influence outcomes. This is called research bias, and it can have the same effect on your findings as any other kind of bias.

There are no concrete rules for researching bias, which means that it can manifest itself at any time. Bias in research can result from both experimental errors and a failure to account for all relevant circumstances.

Research bias is a major source of unreliable findings in scientific studies. Due to the unstructured nature of research, defining bias requires caution. The ability to detect the signs of it can help you take steps to mitigate or prevent its onset. Here, we'll define it, identify its subtype, and discuss preventative measures.

Types of research bias with examples

Practically every component of quantitative and qualitative research contains bias, which can originate from both the survey producer and the participants. The most manageable types of bias in research are those that originate directly from the researcher conducting the study.

Bias can arise in UX research in several ways, including:

  1. Sampling Bias occurs when the sample population does not represent the user base. For example, if a UX researcher only researches a particular demographic group, such as young adults, they may miss the needs of older adults or other groups.

  2. Confirmation Bias: This occurs when a researcher only seeks out information that confirms their pre-existing beliefs or hypotheses while ignoring or discounting information that contradicts their assumptions.

  3. Observer Bias: This occurs when the researcher's own beliefs or values influence the interpretation of user behavior or data.

  4. Social Desirability Bias: This occurs when users alter their responses or behavior to present themselves in a more positive light or to conform to societal norms.

  5. Hawthorne Effect: This occurs when users modify their behavior because they know they are being observed.

  6. Cultural Bias: This occurs when the research is conducted in one culture, and the findings may not apply to other cultures or contexts.

  7. Language Bias: This occurs when research participants do not speak the same language as the researcher, or the language used in the study is not inclusive or accessible to all participants.

  8. Technology Bias: This occurs when a study is conducted using technology that is not accessible or familiar to all participants or when the technology used in the study biases the results.

  9. Expectancy Bias: This occurs when a researcher expects a certain outcome from a study and unconsciously influences the research process or interpretation of results to align with those expectations.

  10. Time and Budget Constraints: Limited resources, time, and budget constraints can influence the quality and scope of research, leading to biased results or incomplete findings.

  11. Researcher’s Personal Biases: Personal biases, beliefs, or assumptions of the researcher can also influence the research process and interpretation of results.

  12. Researcher-Participant Relationship Bias: This occurs when the researcher's relationship with the participants, including their demeanor or communication style, can influence the participants' responses.

  13. Power Dynamics Bias: This occurs when power dynamics, such as the researcher's authority or perceived expertise, can influence the participants' responses or behavior.

  14. Sampling Method Bias: This occurs when the method used to recruit participants may exclude certain groups or biases the sample towards a particular demographic or behavior.

  15. Analysis Bias: This occurs when the researcher's data analysis is not objective or systematic, leading to biased conclusions.

  16. Funding Source Bias: This occurs when the research funding source influences the research design, methodology, or interpretation of results.

To address these biases, UX researchers should use a variety of data collection methods to gain a comprehensive understanding of user needs, consider the potential biases that may arise in each stage of the research process, and document their methodology and analysis process to ensure transparency and reproducibility. It is also important to recognize and address biases when they are identified, whether through stakeholder feedback or critical self-reflection.

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