How to Avoid Bias in Your User Research Analysis: Ways to Minimise Bias

10 May 2024 - By N. Hoeberichts

We all know that user research is a crucial part of product design and development. It involves gathering data and insights from users to inform product development decisions. However, it is unfortunately quite easy to get biased research results for various reasons. All kinds of different biases can skew the results, leading to incorrect conclusions and could therefore lead to poor product decisions and solutions. In this blog post, we will discuss a few ways for avoiding bias in user research.

Define Clear Objectives

Before starting your UX research project, it's important to define clear objectives by formulating specific research questions and research outcomes. What questions do you want answered and what do you want to get out of the research at the end? Research questions help focus the research and ensure that the data collected is relevant to the problem.

After data collection and when you're analysing the data, it's especially important to refer back to these questions to ensure that the insights align with them. Without clear objectives and clear research questions, it's easy to misinterpret the data or make assumptions that are not supported by the research.

Choose the Right Research Method

It's also important to choose the appropriate research method depending on your research objectives. Depending on where you are in the product development phase, different UX research techniques are utilised, also depending on your research goals.

For instance, if you are at the start of a product development cycle (i.e. you are starting from scratch), you need to conduct exploratory research to understand what are your target audience's needs and pain points so that you can come up with an appropriate solution for those unmet needs and pain points.

Another example is, if your objective is to understand how users purchase items on a website and you want to discover how to improve the usability, you may conduct usability testing.

By choosing the right research method, you can ensure that you are collecting data that is relevant to the design problem, making it easier to analyse the data, identify patterns and give sound recommendations.

Use Multiple Data Sources & Triangulation

Using multiple data sources is another way to avoid bias in user research analysis. Relying on a single source of data can lead to biased conclusions. For example, if you only conduct user interviews, you may miss important insights that could be gathered from usability testing or quantitative data. By using multiple data sources, you can triangulate the data to ensure that the insights are consistent across sources. This approach can also help identify any discrepancies in the data, which can be further explored to gain a deeper understanding of the user needs.

For instance, if you're conducting user research for a new mobile application, you may use multiple data sources such as surveys, user interviews, and product analytics data. This will help reduce the risk that one of your data sources (for instance, your qualitative data) has not been 'tainted' by bias, as you're also including other sources of data.

Involve Multiple Researchers in the Project

Involving multiple researchers in the data collection and analysis process is another way to avoid bias. Different researchers may interpret the data differently, which can lead to more comprehensive insights.

It can also be beneficial to include people who are not involved in the design process to avoid confirmation bias. Confirmation bias occurs when someone interprets data in a way that confirms their pre-existing beliefs. By involving researchers and analysts who are not invested in the design process, you can ensure that the data is analysed somewhat objectively.

For example, if you're conducting user research for a new e-commerce website, you may involve multiple analysts from different departments such as marketing, design, and development. This approach can help you gain a diverse perspective on the data, which can lead to more comprehensive insights and a more complete picture.

Use Random Sampling Techniques

Random sampling techniques can also help minimise bias in user research. This involves selecting participants randomly from the target population to ensure that everyone has an equal chance of being selected. Random sampling can help reduce selection bias, which occurs when participants are not representative of the target population. By using random sampling techniques, you can ensure that the data collected is more representative of the target population and therefore more accurate.

For instance, if you're conducting user research for a new healthcare platform, you may use random sampling techniques to select participants from different age groups, genders, and medical conditions. This can help ensure that the data collected is diverse and actually representative of the target population.

Conclusion

It’s important to note that we can never truly get rid of all the bias in our final results. The only thing we can do is to minimise the bias.

However, it is also true that much of the work a researcher does is to make sense of the data and problem in their head, understand the business requirements of their client or company, and putting those together to formulate recommendations to improve the product or service that they are researching. In a way, this means that some bias or subjectivity is actually needed to make good and valuable recommendations to the organisation.

It's crucial to keep in mind that bias can be subtle and difficult to detect, but by implementing these tips, you can minimise the risk of bias and get a more accurate understanding of your users.