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When perusing your social media feeds, have you ever ignored posts by friends with opposing political views? However, you most likely paid attention to the content published by others who share your political views. This is an example of confirmation bias, a phrase coined by psychologist Peter Wason in 1960 to describe the tendency to let existing views influence how we perceive new information.

People choose information that confirms their views and disregards information that opposes them. Using appropriate methods, confirmation bias in UX design can be identified and avoided.

But,

What is confirmation bias?

Confirmation bias definition

Confirmation bias definition goes like:

“Confirmation bias is a cognitive error that arises when people seek or interpret information in a way that directly confirms their preexisting opinions or prejudices. Confirmation bias causes people to dismiss information that contradicts their previous ideas, even if true.”

How people interpret fresh information is an example of confirmation bias in action. When presented with evidence that contradicts their previous views, people may be more likely to disregard or downplay it in favor of information that supports them. As a result, decision-making may become biased and only consider some relevant information.

Confirmation bias tremendously impacts people’s views and interpretations of the world. People can reduce its influence in decision-making by becoming aware of it and seeking different perspectives.

Critical aspects of confirmation bias psychology

1. Selective attention:

Users tend to focus on information confirming their beliefs and ignore or undervalue information contradicting them.
Users might pay more attention to features or elements that align with their expectations or past experiences.

This article will give you in-depth information about selective attention

2. Selective interpretation:

Users interpret ambiguous information in a way that supports their preconceptions.
In UX, this can lead to misunderstandings or product misuse if the design does not align with user expectations.

3. Selective memory:

Users are more likely to remember information that confirms their beliefs and forget information that does not.
This affects how users recall their experiences with a product, potentially skewing feedback and usability testing results.

Confirmation bias examples

1. Onboarding process:

Suppose users believe a software application will be difficult to use. In that case, they might focus on any challenges they encounter during onboarding, reinforcing their belief even if the overall experience is positive.

This can result in users staying on the product early despite its potential benefits.

2. Search and navigation:

Users often rely on familiar search terms or navigation paths. If they believe a specific feature should be in a particular location, they might miss it if placed elsewhere, even if it’s prominently displayed.

This leads to frustration and the perception that the product could be better designed.

3. User feedback and testing:

During usability testing, participants might focus on confirming their preconceived notions about the product. For instance, if they expect a feature to be flawed, they might find faults that align with this belief, overlooking positive aspects.

This can skew test results and lead to misguided design changes.

4. Personalization and recommendations:

Users who believe a recommendation system is not personalized to their preferences might selectively notice and remember when the recommendations are off-target, ignoring when they are accurate.

This can diminish trust in the system and reduce engagement with personalized features.

5. Error messages and feedback:

If users expect a system to be unreliable, they might perceive error messages or system feedback more negatively, interpreting minor issues as significant faults.

This impacts a negative impression of the product, affecting user satisfaction and retention.

When does the confirmation bias occur?

Confirmation bias occurs in several stages of the UX process, including:

1. User research

a. Survey design:

When designing surveys, the way questions are framed can lead respondents to answer in a way that confirms the designer’s expectations. For example, asking “How much do you like feature X?” assumes that users like the feature to some extent.

b. Interviewing:

During user interviews, interviewers might unconsciously steer the conversation towards topics that support their hypotheses or interpret ambiguous statements in a way that aligns with their expectations.

2. Usability testing

a. Test scenarios:

Creating tasks more likely to validate the designer’s assumptions about user behavior can skew the results. For example, if a designer believes a feature is intuitive, they might create a task that overly simplifies its use.

b. Observation:

Observers might focus on behaviors or comments that support their existing hypotheses, overlooking or underestimating contradictory evidence.

3. Data analysis

a. Selective analysis:

Highlighting data points that confirm existing beliefs while ignoring conflicting information can lead to biased conclusions. For instance, reports should present only positive feedback.

b. Overfitting:

Interpreting ambiguous results to fit the desired outcome, such as attributing user difficulties to external factors rather than design flaws.

4. Design decisions

a. Feature prioritization:

Prioritizing features based on biased interpretations of user needs can lead to a product that doesn’t address user problems—for example, adding features that designers like but users don’t need.

b. Iterative testing:

Ignoring negative results or modifying tests to fit the desired outcome can skew the design process. For example, we should dismiss usability issues as user errors rather than address them.

c. Feedback interpretation:

Giving more weight to positive feedback that aligns with the design vision and disregarding negative feedback as outliers can create a skewed understanding of user satisfaction.

Here are some Octet tips that you can use to deliver outstanding UX experiences.

Impact of confirmation bias

When designers or researchers exhibit confirmation bias, they may be more likely to seek data that confirms their preconceived conceptions or ideas about the user rather than considering alternative viewpoints. This may result in a skewed design process, resulting in a product or service that does not meet the user’s needs or expectations.

To mitigate the impacts of confirmation bias in UX design, it’s crucial for designers to actively seek out diverse ideas and evaluate a wide range of perspectives when making decisions. This inclusive approach can include soliciting feedback from several stakeholders, conducting user research with diverse participants, and seeking alternative points of view throughout the design process.

In addition, user and usability testing approaches help gather factual information on how users interact with a product or service. This can help identify design components that users may find frustrating or perplexing and provide more objective information about the design process.

Overall, designers may create more prosperous, user-centered products and services by being aware of confirmation bias and taking steps to reduce its impact.

How do we avoid confirmation bias?

1. Research rather than validate

UX professionals should begin with an open mind and seek to test theories and assumptions rather than validate them. Research aims to discover new information, not to validate previously held beliefs. Designers should be agile and able to rapidly detect when they are on the incorrect track rather than devoting time and resources to delving further into an unpromising design concept.

To avoid this validation cycle, in which designers presume that the test will confirm what they already think to be accurate, remember that the planning portion of any user study should involve a thorough assessment of the test objectives.

2. Collect early data

As previously stated, the less time, resources, and emotions you invest in a particular design, the less skewed your interpretation of user research findings will be. As a result, the sooner you obtain practical data from the target audience, the better you will be relatively objective while interpreting the data and acting on the findings.

3. Ask non-biased queries

When gathering user feedback, whether through usability testing, diary studies, or interviews, UX practitioners should avoid asking leading questions. Leading questions prepare test takers for subjects the researchers may be interested in.

When you have a precise theory, it can be challenging to formulate a non-leading query. Always take a step back and consider whether this question recommends responding to the participant. Can the participant guess my hypothesis based on this question? If the answer is yes, rephrase your query.

4. Use triangulation

Multiple data sources can not only improve the credibility of your research but also reduce confirmation biases. It is easy to twist and flip one study found to fit your premise. Still, it is far more challenging when data comes from multiple sources, such as user testing, analytics, quantitative analyses, or customer service logs.

Assume that the user feedback showed that users had no problems with the checkout process, but the analytics data plainly shows that users are not completing the transaction. The poll should have asked the correct question, neglecting the real cause of the high abandonment rate.

5. Include fresh perspectives in study, planning, and analysis

Whenever possible, invite a colleague not directly involved in your project to review your study design and attend your presentation of the findings. Someone unfamiliar with past beliefs can often provide a new, unbiased perspective and assist you in identifying the consequences of confirmation bias.

Conclusion

Confirmation bias can lead to people holding onto incorrect views or giving more weight than evidence warrants to information that supports their opinions. People may need more confidence in their views because they have amassed evidence to support them, even if substantial data contradicting their beliefs has been disregarded or ignored.

Confirmation bias can confuse judgment, impair the capacity to sympathize with consumers, result in poorly designed research projects, and misinterpret feedback outcomes. Understanding how confirmation bias affects researchers’ output and user reactions enables UX practitioners to employ realistic approaches for gathering meaningful data that lead to well-designed products.

To understand the complete ins and outs of Confirmation Bias in design, read this research paper.

Frequently asked questions:

1. What is confirmation bias vs cognitive bias?

Confirmation Bias refers to the tendency to analyze and remember information in a way that confirms one’s preexisting beliefs or hypotheses. Essentially, people with confirmation bias focus on evidence that backs their views while ignoring or discounting evidence that contradicts them.

Cognitive Bias, on the other hand, is a broader term encompassing a wide range of systematic patterns of deviation from standard or rationality in judgment. These biases can affect decisions and judgments that are illogical or skewed due to various mental shortcuts (heuristics), social pressures, or emotional influences.

2. How to avoid confirmation bias?

To avoid confirmation bias, individuals can take several steps, including:

Recognizing and being aware of one’s biases: The first step is acknowledging that confirmation bias exists and understanding how it may influence decision-making.

Seeking diverse perspectives: Seeking information that challenges your beliefs can help counter confirmation bias.

Being open to changing your mind: Cultivating a mindset open to adjusting beliefs based on new evidence can help mitigate the effects of confirmation bias.

Engaging in critical thinking: Evaluating information objectively and considering alternative explanations can help reduce the influence of confirmation bias.

Encouraging constructive debate: Creating an environment where different viewpoints are welcomed and debated can help prevent confirmation bias from taking hold.

3. Is confirmation bias a selection bias?

Confirmation bias and selection bias are related but distinct concepts. Confirmation bias is the preference to favor information that confirms one’s beliefs while ignoring or minimizing contradictory information. This bias operates at an individual cognitive level, influencing how people process and interpret data based on their preconceived notions.

Selection bias, on the other hand, emerges when the sample or data selected for analysis is not symbolic of the broader population, leading to skewed or invalid conclusions. This bias typically arises in research and data collection when selecting participants or data points and introduces systematic differences.

Confirmation bias explained-Importance & examples

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Aakash Jethwani

Inspire the next generation of designers

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