Eight underrated biases in product research and how to address them
As humans, we are innately wired to rely on cognitive shortcuts and heuristics to simplify the complex world around us. These biases, while often serving a practical purpose, can also distort our decision-making, perception, and judgment in various domains, including design research. As design researchers, our ability to recognize, understand, and navigate the landscape of biases is essential to developing insightful and impactful solutions. It is our responsibility to critically examine our thought processes, question our assumptions, and continually strive for improvement.
Design research, at the intersection of human-centered design, psychology, and business processes, is particularly vulnerable to the influence of biases. The field demands a delicate balance between empathy for users, understanding of organizational context, and the pursuit of innovation. Consequently, design researchers often encounter a wide array of biases that are unique to their context, influencing all stages of the research process — from initial problem formulation to data interpretation and solution implementation.
Besides the obvious biases that affect us all — confirmation bias, selection bias, etc, I have found that in the problem-solving disciplines we are there are a few specific biases that we can point to.
Developing an awareness of these design research-specific biases allows us to “debug” our work, improving the quality and effectiveness of our outcomes. By actively identifying and addressing these cognitive pitfalls, we can enhance our decision-making, cultivate more accurate and meaningful insights, and ultimately create more innovative and user-centered solutions. This self-awareness is not only a powerful tool for personal growth but also an essential component of the broader evolution of the design research field.
1. The (lack of) originality bias
The tendency to underestimate our creativity.
When we are asked to generate ideas on a topic, after doing some research and jotting down a few ideas, these start to seem so obvious that we may think that anyone else would have come up with the same. And yet, this very rarely happens, and most people turn out to come up with different ideas. This happens even if all have the same research material available and is working off the same data. Our mind is more unique than what we think.
2. The shminsight
Mistaking an assumption for an insight.
When we start a project, there are often unspoken assumptions packed into our brief or our “knowledge baseline”. In most cases, simply making these assumptions explicit does not count as an insight. By definition, an insight needs to contain novel information, needs to surprise us. So a counter-intuitive consequence of our assumptions can be an insight, as can surprising trends and data patterns.
A good way to identify insights is to look for tensions: we have trend A and simultaneously, we have counteracting trend B. This is almost guaranteed to yield something interesting. And speaking of intersting…
3. The interesting bias
Preferring the interesting over the true.
We are attracted to interesting concepts. And by interesting, I mean concepts that contradict common sense, but not too much; that are counterintuitive in our way of thinking, but not enough to make us completely change paradigm. This is often the case with stuff like pop psychology, for example. The argument always goes: “most people think that there is no relationship between x and y. Studies show that actually x and y are correlated! Here is what you should do about it”.
In fact, the effect could be minor, and it may disregard the wider picture. Truth is often boring, predictable and conforming to common sense.
This bias even affects the way academic papers are selected for publishing, so it can be downright dangerous.
4. Consumer bias.
Thinking of B2B research as B2C research
Particularly relevant to designers. Because we are human centered and we are more familiar with the consumer’s experience thank with business processes. But in B2B projects, being human-centered does not necessarily mean that we focus on the final user: it means that we see the human behind the company making the decision, creating the product and/or using the product. Sometimes it’s perfectly ok for a good to be completely invisible or a commodity to the final user. It’s a subset of…
5. The empathy bias
Thinking that we understand what the user is thinking, while we really don’t
Designers try to empathize with users, that’s their job. But sometimes the user may be very different from the researcher. Their motivations may be very hard or impossible for the researcher to understand. Philosopher Paul Bloom wrote an entire book about how empathy can be a misleading emotion and confuse our moral intuitions. It can also cloud our judgment as researchers when we think we understand where the user is coming from emotionally, whereas a completely different culture and life experience may mean they are in a completely different place intellectually and emotionally.
6. Early insights bias
Converging prematurely on a set of learnings, at the expense of potentially contradicting later insights
Early in the research process, our learning curve is very steep. Each new piece of knowledge we acquire, however minor, contributes greatly to our understanding of the problem space. Later on you start getting diminishing returns from research, as patters start to emerge more starkly. In information theory terms you could say that the same piece of information, when acquired earlier in the project has higher entropy or information content; if it’s acquired later, it has lower information content. In this phase we need to be careful of not giving the insights we acquired early in the project undue weight, and to just force-fit new information into the earlier insights. This creates a tradeoff between time to delivery and project quality: elaborating insights mid way through the research saves us time and helps to start converging. To be rigorous, however, you should first collect information, then derive insights.
7. Meaning drift
Redefining concepts based on convenience rather than rigor.
At the beginning of the project, concept A (which can be an insight, a pain, a part of a framework, etc.) has a range of meanings ranging from 1 to 10. As the project progresses, we focus on one aspect of this meaning (eg. 2–4), without properly addressing the point and explicitly “rescoping” the concept. So when something interesting to say comes up in the 5–10 range, we are in trouble: do we add it back? Do we put under a separate category? While redefining a term or a concept can be fine and even useful, it is essential to be honest with ourselves and make sure that, when we redefine concepts, we review everything that we previously categorized as falling under that concept.
8. The curse of knowledge
Loosing our “beginner mindset” and ability to challenge assumptions as we acquire domain knowledge
The more we know about a subject, the harder it becomes to innovate. A good way to get around this may be to write everything you think you know or you guess or imagine about a sector before you dive into the research. There could be radical ideas coming from a beginner mindset, and it will be hard to reproduce them once you have learned more.
Identifying and addressing the numerous biases that can surface in design research is critical for ensuring the reliability and effectiveness of our findings. By becoming more aware of biases such as the apparent obviousness bias, the shminsight, the interesting bias, consumer bias, early insights bias, empathy bias, meaning drift, and the curse of knowledge, we can cultivate a more mindful approach to our work. This heightened awareness not only promotes better decision-making and richer insights but also contributes to the overall growth and development of research field. As we continue to refine our methods and practices, taking these biases into account will help guarantee that our work remains rooted in reality while also pushing the boundaries of what is possible.
