1 • The Confirmation Bias
We tend to listen more often to information that confirms the belief they already have, generally rejecting information or, at least, viewing it prejudicially, when that information does not align with their current beliefs on a subject.
In studies, even when two people are provided with the same information, they can come away with completing opposing views of the subject, demonstrating how their existing beliefs creates a bias in how they interpret that information.
How this affects you as a product leader? When we have bet all of our teams resources on pursuing a specific strategy (a new feature, let’s say), because we really want that feature to work, there is a danger we pick up on any evidence — however small or irrelevant — to confirm that this strategy is working. We could, for example, use a vanity metric of ‘number of downloads’ to make us feel better, rather than looking specifically at how many people are actually using the feature.
2 • The Hindsight Bias
We commonly view random events in the past as more predictable than they actually are, creating a logical — but entirely fictional — story in our mind of how something happened and why.
Think of how many times you watch a quiz show with no idea what the answer is until the contestant provides it (and you shout “I knew that!” when you almost definitely didn’t know.)
How this affects you as a product leader? If we don’t hold ourselves accountable to our previous predictions (say, regarding whether a new feature will increase retention or the number of paying customers), then it is very easy to “misremember” past predictions and believe that our predictions regarding what will work — and what will not — are always correct. Even more dangerous, without accountability, we can always easily misattribute growth to a feature change, when it may be entirely unrelated (for example, maybe an article came out about ‘journaling’ in the NY Times & your journaling app saw a massive boost in usage), using that misinformation to inform other decisions in future.
3 • The Anchoring Bias
We tend to be overly influeneced by the first information we hear, referred to as an anchoring bias.
Think of salary negotiations, or the exhorbitant starting prices of some market vendors, who know the art of price negotiation extremely well.
How this affects you as a product leader? It can be very tempting to focus our efforts around the first bit of user research or feedback we receive when launching a new product. If this is the case, ask yourself, are you building for the right audience? What was the context of that information? What other biases may be in play to impact that decision?
Just because your new users seem to have used the product for a few minutes in their first session, does that mean it provides value? Or did they just use it because they know you? Because you had a personal call or onboarding session with them? It’s a dangerous place to be continuing to work based on an as-yet unvalidated core assumption.
4 • The Misinformation Effect
Our memories of particular events also tend to be heavily influenced by things that happened after the actual event itself, a phenomenon known as the misinformation effect. A person who witnesses a car accident or crime might believe that their recollection is crystal clear, but researchers have found that memory is surprisingly susceptible to even very subtle influences.
How this affects you as a product leader? It’s important to document your product decisions (what you built, why & what was the actual outcome of that experiment) precisely for this reason. If we misattribute — or “misremember” — why something worked for us in the past, we are in danger of building future product features based on a completely fictional understanding of what value we can provide for our customers.
5 • The Actor Observer Bias
We perceive our actions differently to the actions of others, tending to give ourselves the benefit of the doubt far often than we do others.
For our own actions, we happily attribute outcomes to external influences. When we were late for a meeting, it was the traffic. When we failed to make that sale, it was the jetlag or the unreasonable character of the client.
For others, we blame internal causes. When our colleague was late, it was because they are lazy and disorganised. When they failed to make that sale, it’s because they were incompetent & less intelligent.
How this affects you as a product leader? We should take decisions that are always able to provide us with feedback. If we build a new feature, build and launch it quickly, track the results, then use that feedback to inform your future decisions. After a few repetitions, it’s hard to hide behind excuses or biases, when you can clearly see the positive or negative outcome of your work over the last few weeks.
6 • The False-Consensus Effect
We overestimate how much other people agree with their own beliefs, behaviors, attitudes, and values, an inclination known as the false consensus effect. This can lead people not only to incorrectly think that everyone else agrees with them — it can sometimes lead them to overvalue their own opinions.
This tends to stem from the people in our immediate family & friends, who we tend to share common beliefs with, as well as a need for self-worth that comes from wanting other people to be just like us.
How this affects you as a product leader? I’ve observed that, quite often, there is an illusion of alignment in product teams that can be very dangerous. We may believe we are talking about the same thing (for example, launching a Minimal Viable Product, or deciding upon a specific product strategy), but our interpretation of these terms or of some piece of data can be wildly different. In order to better align yourselves, question the other person (as well as your own thinking) to clarify you are closely aligned.
7 • The Halo Effect
We rate good-looking people as smarter, kinder, more competent, more qualfiied and funnier.
How this affects you as a product leader? When hiring, be aware simply that this bias exists and try to look as objectively as possible at the existing data. Has the candidate shown evidence of taking the initiative, learning quickly, delivering results? Or do they just look like the kind of person that delivers results?
8 • The Self-Serving Bias
We tend to give ourselves credit for successes but lay the blame for failures on outside causes.
Our success is attributed to hard work and smart thinking. When things go wrong, it was bad luck or some unforeseeable circumstance that thwarted our success.
How this affects you as a product leader? In the inherently uncertain world of an early-stage startup, we must learn to experiment, fail & learn in quick iterative cycles. If we protect our ego by blaming the failure of an idea as “bad luck”, then we fail to learn. When we fail to learn, we fail to progress towards building something that provides value for customers and, ultimately, provides enough value that those customers are willing to pay for it. Better, instead, to accept that failure is part of the process — and not a hit to your ego.
9 • The Availability Heuristic
We distort our perception of reality based on the availability of information.
Say we read two articles about an increase in knife crime in our neighbourhood, we may quickly come to the conclusion that crime is massively on the rise, despite no objective data supporting this thought.
How this affects you as a product leader? As a startup founder, it’s very easy to assume that the only route to success is to raise investment & build the next billion-dollar company, as these success stories are pasted all over tech magazines & conferences all over the world. The objective reality, however, is that very few venture-backed startups succeed because of the exceptionally high demands on consistent, month-on-month growth.
10 • The Optimism Bias
We tend to overestimate the likelihood that good things will happen to us while underestimating the probability that negative events will impact our lives. We assume that events like divorce, job loss, illness, and death happen to other people.
How this affects you as a product leader? We confidently assume we won’t be part of the 80–90% of startups that fail, not bothering to really ensure that we are on the right track towards success, not even bothering to enact measures to increase our chances of success by, for example, focusing on building an adaptive, open culture, or implementing a lean product development process.