Representing users, transforming services | Polar Insight
Representing users, transforming services | Polar Insight
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Five tips for avoiding bias in user research

In this article, we offer five ways that you and team can reduce the likelihood of bias impacting your research results.

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When conducting research, it is virtually impossible to separate yourself completely from the data you want to collect. We all have known and unknown biases and these can impact the information that is collected and how it is analysed.

Some of the most commonly observed biases include:

  • selection bias

  • self-selection bias

  • recall bias

  • observer bias

  • survivorship bias

  • omitted variable bias

  • cause-effect bias

  • funding bias

  • cognitive bias

We highly recommend a quick Google search of the different biases outlined above. There’s a wealth of information out there worth investigating and it will transform how you think about gathering and interpreting information.

To get you started, we’ve pulled together a list of five approaches you can use to avoid bias and to make your research as objective as possible.

1. Use multiple and different data sources

Firstly, the use of multiple data sources - otherwise known as ‘triangulation’ - is a common method used to support the interpretation of data. By linking several data sources together you can compare and contrast, find similarities and build confidence that your findings are legitimate.

2. Use multiple and different people to interpret the data

Secondly, we recommend that you engage several people in interpreting your data. Consistency between interpretations improves the likelihood your findings are a fair representation of what is happening.

3. Ask participants to review your results

Another good method to avoid bias is to ask the participants of your research to read back your results and ask them whether your interpretations seem to be representative of their beliefs. A great way to double your results, this has the added benefit of empowering your users.

4. Examine alternative explanations

Always consider whether there are alternative explanations for your data. If you are careful to rule out or account for as many of these alternative explanations as possible, any interpretations will be more robust.

5. Review your findings and conclusions with others

Finally, ask others to review your conclusions. If you can, share your findings with people outside of your immediate peer group or department. A fresh pair of eyes can be hugely valuable.

Anything we’ve missed?

If you like to use any other methodologies to reduce bias in your work, we’d love to hear about them in the comment section below.

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