11 June 2026
Is confirmation bias ruining your digital product?
What is confirmation bias? It might be the reason your app isn’t working the way you expected. Find out how to avoid falling into this cognitive trap.
You’ve spent months working on the launch of a new feature for your digital product. You came up with it, validated it, designed it, and analyzed all the data available to you. It was supposed to be the turning point, you were sure active users would love it and that waves of new users would flood into your app. Finally, the launch happens and… nothing.
How is it possible that a feature that seemed so important turns out to be a complete flop? One possible cause is confirmation bias. But what is it, and how does it affect a digital product? Let’s find out in the next paragraphs.
What is confirmation bias
Confirmation bias is a cognitive pattern that leads us to confirm opinions (or prejudices) we already hold. Let’s take an example: if we think that all programmers are men, nerdy, and with unkempt hair and beards, finding ourselves in a room full of developers (for example, at Codemotion), we won’t notice all the female participants or those with a fresh haircut from the barber.
In practice, we tend to pay more attention to examples that confirm our hypotheses. As a result, we tend to overlook data that would instead disprove them. And this is not a voluntary process; it is a strategy we automatically apply when interpreting the world around us.
This selection of information, like everything that happens in our brain, is not random: like other cognitive biases, its function is to reduce cognitive load. Confirming a familiar story, an already established view, allows us to avoid the mental fatigue that comes from having to reassess our positions. And we have already discussed in another article how we all aim to minimize energy expenditure.
How confirmation bias affects your digital product
It may seem surprising that this mechanism can affect your application. Yet, it inevitably does, and it can potentially act at different stages of the work.
Let’s look at some examples across different stages of developing a new feature:
- During the ideation phase, an idea for a new feature you’ve strongly fallen in love with may be defended at all costs, even when no data seems to support its usefulness. You end up starting from the solution to derive the problem, even when the latter doesn’t exist.
- Validation may take place by considering only positive feedback; unconsciously, you may give less weight to criticism of the feature compared to the opinion of those who appreciate it.
- During the feature design, only variants deemed plausible may be tested, meaning those that confirm the feature should appear in a certain way.
- Finally, in data analysis, KPIs may be chosen based on what you expect the feature to produce, rather than evaluating which ones are actually the most important for the product.
In practice, any stage of the process can potentially be affected by confirmation bias. The consequence is a situation like the one described in the introduction: everything seemed set up for great success, yet the result is disappointing.
Relying on data is not enough
Can confirmation bias be avoided by following a data-driven approach? After all, if your decisions are based on data, isn’t it the data that speaks? Yes, but not always reliably. Let’s see why.
Confirmation bias leads to:
- Seeking information that confirms the theory
- Selectively remembering elements that support your idea
- Interpreting data as confirmation of your idea
In concrete terms, this can mean:
- People interviewed to gather feedback are asked questions that already suggest the answer. We discussed this in detail in another article.
- The only case studies considered during a comparative analysis are those that support the initial idea.
- A decision is made with limited data: if after a few interviews weak signals of interest in the feature emerge, no further interviews are conducted.
- Potential issues are not investigated: if a contrary opinion is expressed in an interview, or if a positive response could be explained by other factors, these potential critical aspects are not explored further.
In all these cases, it cannot be said that objective criteria were not used, but the method of collection and analysis is still affected by bias.
And that’s not all: in a company context, the risk is that this misinterpretation of data becomes a collective dynamic that reinforces itself in the group’s belief that it is acting deliberately.
How to prevent confirmation bias from ruining your app
Confirmation bias is unconscious and automatic. Does this mean we have to accept that it will affect our application? Of course not. But it does mean we need to pay attention to it and recognize when it appears.
How to tell if decisions are not objective
The first step in counteracting the effects of confirmation bias is being able to recognize it. Certain phrases can be fairly reliable indicators. Some examples include:
- “Users didn’t understand the feature”
- “Okay, but that case is an exception”
- “As expected, the data shows…”
Why are these phrases problematic? Because they reveal three different issues: a misattribution of failure, a lack of consideration for elements that contradict one’s idea, and the presence of an expected hypothesis that is defended before analyzing the data.
Another sign that preconceptions are driving decisions is when decisions come before evidence: for example, moving forward with implementing a feature aimed at a certain user segment because it is assumed they want it, without first investigating their actual needs through interviews, surveys, or by reviewing feedback.
But don’t worry: it is always possible to bring the process back on track, as long as you are willing to adopt effective countermeasures.
The antidote to confirmation bias
Once you learn to recognize when bias is affecting your product, you also need to know how to protect the impartiality of your decisions.
Although there is no single way to fight confirmation bias, we have gathered some suggestions that we at Mabiloft have experienced firsthand, and that we still implement today to avoid falling into this distortion of the process.
Don’t fall in love with your idea | It’s easy to get attached to an idea, especially if we were the ones who put it on the table. But we should always try not to cling to an idea from the start. Reducing initial commitment helps us validate without prejudice. |
Actively seek disagreement | This doesn’t mean always being contrarian, but rather often taking on the role of the devil’s advocate. Constantly challenging ideas is the best way to learn how to defend them, but also to understand which ones are truly valid. For this to be possible, however, a company culture of open discussion is necessary, where everyone must feel free to express disagreement without it creating resentment. |
Ask the right questions | Sometimes the temptation is to ask questions as if we already had the answer, for example asking: “What do you like about this feature?” instead of “What do you think about this feature?”. Learning to ask the right questions, which do not steer the answers, is essential to obtaining unbiased responses. |
Use data fairly | We have seen that even data can be bent to obtain specific answers. But to have an objective point of view, it is necessary to rely fully on what the data tells us, for example by defining KPIs before seeing the results. To do this, we must also accept that we will sometimes get uncomfortable answers. |
In conclusion, it is normal for a complex process like creating and maintaining an application to run into this type of bias. But for the product to succeed, it is essential to recognize it and take corrective action. Only in this way will the final product truly be able to meet users’ needs.
What about you? Do you think your product’s growth has been slowed down or limited by this bias? If you have a digital product you want to grow, we can help you. Get in touch with us with no obligation or visit the dedicated website.
