What are the most common research biases and how can you avoid them?

1 Confirmation bias

Confirmation bias is the tendency to seek, interpret, and favor information that confirms your existing beliefs or hypotheses, while ignoring or rejecting information that contradicts them. This can lead to selective attention, confirmation-seeking questions, cherry-picking evidence, or overconfidence in your conclusions. To avoid confirmation bias, you should try to be aware of your own assumptions and expectations, seek out diverse and contradictory sources of information, test your hypotheses with objective and rigorous methods, and be open to feedback and alternative explanations.

2 Sampling bias

Sampling bias is the error that occurs when the sample of participants or data that you use for your research is not representative of the population or phenomenon that you want to study. This can lead to skewed or inaccurate results that cannot be generalized or replicated. To avoid sampling bias, you should try to use random, stratified, or proportional sampling techniques, ensure that your sample size is large enough and appropriate for your research question, avoid self-selection or convenience sampling, and check for any missing or outliers data.

4 Framing bias

Framing bias is the error that occurs when the way a question, problem, or situation is presented or worded influences the way people perceive and respond to it. This can affect the interpretation, evaluation, and decision-making of the participants or the researcher. For example, people may react differently to a message that emphasizes the positive or negative aspects of a choice, or to a question that uses different scales or units of measurement. To avoid framing bias, you should try to use consistent and standardized language and formats, avoid leading or suggestive words or images, test different frames and compare the results, and be aware of your own cognitive biases.

5 Analysis bias

Analysis bias is the error that occurs when the data analysis or presentation is influenced by the researcher's preferences, expectations, or hypotheses, rather than by the actual data. This can lead to selective reporting, manipulation, or misinterpretation of the data, or to overlooking or dismissing important findings. To avoid analysis bias, you should try to use transparent and reproducible methods and tools, follow ethical and scientific standards and guidelines, report all the relevant data and results, and use visual aids and statistics appropriately.

6 Publication bias

Publication bias is the error that occurs when the availability and accessibility of research findings are influenced by the direction, magnitude, or significance of the results. This can lead to a distortion of the existing literature and knowledge, as positive, novel, or statistically significant results are more likely to be published, cited, or disseminated than negative, null, or inconclusive results. To avoid publication bias, you should try to register your research protocol and hypotheses before conducting the study, report all the outcomes and limitations of your research, submit your work to peer-reviewed and reputable journals or platforms, and conduct systematic reviews and meta-analyses of the literature.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?