What are common biases to avoid in user research?

Create a good screener to recruit your sample. That's the key to obtain an insightful research data. In my experience I have seen many researchers uses convenience sampling, however there is no better choice than random sampling, in this method every member of the population has an equal chance of being selected. However, this is time consuming. In a fast paced environment it is also important to balance research and launch deadlines.

I would like to add cultural Bias here. since cultural differences can significantly impact user behavior and preferences. Ensure your research accounts for cultural variations in your user base.

Many times, this bias creeps in unknowingly - floating online surveys ignoring offline audience, feeling happy about volunteer participants turning up in the study enthusiastically, and so on. As researchers, we have to be very careful in the way we frame our recruitment criteria.

In my experience, this is most commonly encountered when a founder struggles to recognize that they are not the target audience, and resists information from sales or marketing that does not align with their expectations of how the customer "should" or "should not" engage with products or services offered by their organization. When in reality, the product or service should be iterated based on that user feedback, often times it is ignored or shunned as a failure to attract the right customers. This is why the data curated from multiple tests and perspectives is critical to be conveyed in as neutral way as possible. Making a case to management for adjusting an offer will be more powerful when multivariable testing has occurred.

Observer bias is a pitfall in user research to be aware of. Researchers' preconceived notions can inadvertently affect the study. Counter it by training observers to remain objective, record data without interpretation, and triangulate findings with multiple researchers. This ensures that user research results remain unbiased and reliable.

Interpretation bias can taint user research. Avoid it by adhering strictly to a predefined research plan, using standardized data analysis methods, and involving multiple researchers for cross-validation. Keep interpretations based on data, not personal views, to ensure your conclusions accurately reflect user experiences and needs.

The tension between planning and executing a test is where action bias takes root. Beyond documenting your goals and clearly articulating what is being measured, the operators of the test as well as each interested stakeholder should also all be aligned on vision. Having folks on the team that like to "build the plane as it launches from a cliff" mixed with people that prefer to perfectly plan each stage while accounting for endless variables then this tension will be far more great. In my experience, balancing these perspectives to reduce action bias by staying focused on the greater vision is the most effective strategy.

Adding two more biases to the list Social Desirability Bias: Users may provide responses that they believe are socially acceptable or align with the researcher's expectations. Encourage honest and open feedback by creating a safe and non-judgmental research environment. Anchoring Bias: Presenting users with biased or leading questions can anchor their responses. make sure your questions are neutral and don't lead users toward a particular answer.

To minimize these biases, it's essential to follow rigorous research methodologies, diversify your data sources, use a combination of quantitative and qualitative research methods, and continually reassess your research process for potential biases. Additionally, involving multiple researchers and conducting peer reviews can help in identifying and mitigating biases in user research.

In user research, steering clear of biases is paramount. Confirmation bias, where you seek to validate existing beliefs, can misguide findings. Avoid selection bias by selecting a diverse participant pool. Be mindful of the anchoring bias, which can influence questions. Maintaining objectivity and open-mindedness is key to credible user research.