Cognitive biases can have a significant impact on the product development process. When a UX team is unaware of their own biases, they risk reaching incorrect conclusions.

But what exactly is a cognitive bias? 

A cognitive bias is a type of systematic error in thinking that affects one's choices and judgments. Amos Tversky and Daniel Kahneman (of the ‘Thinking, Fast and Slow’ fame) proposed the concept of cognitive bias in a 1974 article in Science. Since then, researchers have discovered and studied a wide range of cognitive biases that shape our perceptions of the world but can also lead to poor decisions. There are hundreds of cognitive biases, according to Wikipedia. We'll look at six of them that are more commonly encountered in user research.

An image depicting a drawing that represents implicit bias

Implicit bias

In popular culture, this is referred to as stereotyping. These are the attitudes and stereotypes we unconsciously associate with people. Implicit bias is extremely difficult to eradicate. This is because it has been instilled in our minds. From a very young age we have been influenced by the media, people around us, and popular culture.

For example:

As UX researchers, we may unintentionally favour findings that confirm our initial assumptions rather than accepting what data represents. Worse, we may design our research to focus on what will most likely confirm our hypothesis (i.e. confirmation bias).

How to avoid implicit bias?

  • Concentrate on the questions and the desire to understand the users
  • Do not look someone up prior to a research session.

Framing effect

Framing biases occur when people make decisions based on how information is presented rather than just the facts themselves. As a result, the same facts presented in different ways may result in people making different decisions or outcomes.

For example:

A leading question such as "Do you like this feature?" frames the response in terms of the positive aspects of a user experience. "What do you think about this feature?" works much better. This is because it allows the user to think about their user experience in a broader sense.

A photograph depicting a notebook with confirmation bias written on a page

Confirmation bias

One of the most common biases that a user experience researcher encounters. We prefer data and insights that confirm our current hypotheses and tend to disregard anything that contradicts them.

For example:

Users may express dissatisfaction with a product's navigation. However, the researcher may disregard such feedback because the design appears to them to be rational.

How to avoid

  • Pay attention to what the users do rather than what they say
  • Use open-ended question framing techniques.

Sunk cost fallacy

Sunk cost fallacy is related to commitment bias. This occurs when we continue supporting previous decisions despite evidence indicating they are not the best course of action. We overlook the fact that any time, effort, or money we have already invested will not be reimbursed.

For example:

If you've heard the phrase "cut your losses" it's a warning to avoid falling victim to sunk cost fallacy.

How to avoid

  • Before the session, write down your assumptions and feelings.
  • Never ignore negative feedback
  • Analyse research with others to avoid favouring specific insights.

Social desirability

When people are around other people, they tend to make more "socially acceptable" decisions. When left alone and acting independently, a person's behaviour may be completely different. As a researcher, you must be aware that the answers you receive during interviews may not be valid. This is because test participants are motivated to provide desirable responses.

For example:

Users may experience social desirability bias despite their true feelings.

How to overcome

As much as possible, try to observe users in their natural environment. Ideally observe them in the same conditions under which they would use the product.

An image representing clustering illusion bias

Clustering illusion bias

The process of clustering involves grouping or categorising massive amounts of data according to their connections. Clustering illusion is the bias that arises from seeing a trend in random events that occur in clusters. The clustering illusion bias is often called the “hot hand fallacy” and is often the source of gambling fallacies.

For example

When analysing data, novices frequently create incorrect clusters and have a propensity to identify patterns where none exist.

How to overcome

  • Use a sufficient and diverse set of users
  • Analyse research with others to avoid favouring specific insights.

An illustration of the author
Petros Lafazanidis
Nov 2022

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