We are all biased… anyone who says otherwise is fooling themselves or lying. We are all biased because we are all human… We are the sum of all of our experiences, and those “inputs” influences our opinions and interpretation of facts.
As user experience professionals, we are going to encounter biases in our research subjects and in ourselves, when we analyze the information we have gathered through usability tests or research. Understanding the type of biases that we all have will help you “get over” them and be objective when analyzing your data.
Taken from Wikipedia, here’s some of the key biases to watch out for…
Anchoring: The tendency to rely too heavily, or “anchor,” on a past reference or on one trait or piece of information when making decisions. As an example, I’ve seen this in users who won’t try out mobile banking because their first experience was so bad.
Bandwagon Effect: the tendency to do (or believe) things because many other people do (or believe) the same. You see this all the time when it comes to user’s opinions about technology and certain tech companies.
Confirmation bias: The tendency to search for or interpret information or memories in a way that confirms one’s preconceptions. This is why people tend to get their news from sources that reinforces their world view (CNN, Fox News, talk radio, etc.)
Curse of Knowledge: When knowledge of a topic diminishes one’s ability to think about it from a less-informed perspective. I’ve seen this in a lot of user researchers, who can’t “unlearn” what they know and look at user data from a fresh perspective.
Empathy Gap: The tendency to underestimate the influence or strength of feelings, in either oneself or others. This is why empathy is a key “soft skill” user experience professionals need to have.
Framing Effect: Drawing different conclusions from the same information, depending on how or by whom that information is presented. This is why con men will always exist.
Hindsight bias: Sometimes called the “I-knew-it-all-along” effect, the tendency to see past events as being predictable at the time those events happened. Remember, kids, correlation is not causation…
Irrational Escalation: Where people justify increased investment in a decision, based on the cumulative prior investment, despite new evidence suggesting that the decision was probably wrong. (Irrational Escalation was actually the original name of Las Vegas.)
Selection Bias: The distortion of a statistical analysis, resulting from the method of collecting samples. If the selection bias is not taken into account then certain conclusions drawn may be wrong. This is why you need to be very thoughtful when it comes to recruiting participants for user research and testing.
Stereotyping: Expecting a member of a group to have certain characteristics without having actual information about that individual. This is, unfortunately, still a very persistent cognitive bias in people around the world.
(Lot’s more here: http://en.wikipedia.org/wiki/List_of_cognitive_biases)
Avoid bias two ways: First by, educating yourself on the biases that we have (and hopefully this list is a good start) and second, when doing any analysis have more than three people involved so that they can “police” each other. It may not mitigate all the bias, but it will help minimize any impacts the bias has on the results.