In 1981, Amos Tversky and Daniel Kahneman explored how different phrasing affected participants’ responses to a choice in a hypothetical life and death situation. In the study, participants were asked to choose between two treatments for 600 people affected by a deadly disease. Treatment A was predicted to result in 400 deaths, whereas treatment B had a 33% chance that no one would die but a 66% chance that everyone would die. This choice was then presented to participants either with positive framing, i.e. how many people would live or with negative framing, i.e. how many people would die. Treatment A was chosen by 72% of participants when it was presented with positive framing (“saves 200 lives”) dropping to 22% when the same choice was presented with negative framing (“400 people will die”).

The choice of the participants in this study highlights our tendency to make decisions based on whether the options are presented with positive or negative connotations; e.g. as a loss or as a gain. In psychology, this is known as the framing effect and it affects all aspects of the design process, from interpreting research findings to selecting design alternatives.

During World War II, the Statistical Research Group at Columbia University was asked by the U.S. military to examine the damage done to bombers that had returned from missions. Their objective was to determine where armor could be added to the bombers to increase the protection from the flak and bullets. Planes that returned were carefully examined and damage was compared across all the planes. The consensus was that additional armor should be added to the areas of the planes that show the most common patterns of damage: the wings, tailfin, and middle of the planes.

Luckily, a statistician on the project by the name of Abraham Wald countered this conclusion by pointing out that the planes being examined were only those that survived, therefore calculations were missing a critical set of data (the planes that didn’t make it back). As a result, Wald recommended that armor get added to the areas that showed the least damage to reinforce the most vulnerable parts of the planes.

The case study of World War II bombers highlights our tendency to concentrate on the people or things that made it past a selection process and overlook those that do not, typically because of their lack of visibility. This can lead to false conclusions in several different ways. This tendency is known as survivorship bias and it can show up during a critical part of the design process.

People who hold strong opinions on complex social issues are likely to examine relevant empirical evidence in a biased manner. As a result, they will accept confirming evidence at face value while critically examining evidence that disconfirms their opinions. This was the conclusion of a study conducted in 1979 at Stanford University concerning capital punishment.3 In the study, each participant read a comparison of U.S. states with and without the death penalty, and then a comparison of murder rates in a state before and after the introduction of the death penalty. The participants were asked whether their opinions had changed. Next, they read a more detailed account of each comparison’s procedure and had to rate whether the research was well-conducted and convincing. Participants were told that one comparison supported the deterrent effect and the other comparison undermined it, while for other participants the conclusions were swapped. In fact, both comparisons were fictional.

The participants, whether supporters or opponents of capital punishment, reported shifting their attitudes slightly in the direction of the first comparison they read. After reading the more detailed descriptions of the two comparisons, almost all returned to their original belief regardless of the evidence provided, pointing to details that supported their viewpoint and disregarding anything contrary. The results illustrated that people set higher standards of evidence for hypotheses that go against their current expectations.

We tend to search for, interpret, favor, and recall information in a way that confirms or supports our prior beliefs or values. This tendency is known as confirmation bias, and when we aren’t careful it can also show up during the design process as well.

A study published in 2006 by Emily Pronin and Matthew Kugler explored how people make judgments about themselves and others. The study began with the researchers explaining cognitive bias to the participants. Next, they were asked how might cognitive bias might affect their judgment concerning the other participants. What the researchers found was that test participant rated themselves as less susceptible to bias than others in the experiment. When they were asked to explain the judgment of others, they did so openly. In contrast, when they were asked to explain their own judgments, they looked inward and searched their thoughts and feelings for biased motives. The fact that biases operate unconsciously means that this introspection was not informative, but people mistakenly interpret them as evidence that they are immune to bias.

We believe that we are rational, that our actions and judgments are accurate, and they are not influenced by unconscious bias. However, the study described highlights the tendency for people to not see their own biases and see themselves as less susceptible to bias than others. In psychology, this is known as a bias blind spot, and its potential to undermine the decision-making process has a profound impact on design teams.

A study conducted by Amos Tversky and Daniel Kahneman in 1982 asked a sample of 245 University of British Columbia undergraduates to evaluate the probability of several catastrophic events in 1983. The events were presented in two versions: one that included only the basic outcome and another that included a more detailed scenario leading to the same outcome. For example, half of the participants evaluated the probability of a massive flood somewhere in North America in which more than 1000 people drown. The other half evaluated the probability of an earthquake in California, causing a flood in which more than 1000 people drown. A 9-point scale was used for the estimations: less than .01%, .1%, .5%, 1%, 2%, 5%, 10%, 25%, and 50% or more.

What Tversky and Kahneman found was that the estimates of the conjunction (earthquake and flood) were significantly higher than the estimates of just the flood (p < .01, by a Mann-Whitney test). Even though the chance of a flood in California is smaller than that of a flood for all of North America, participants estimated that the chance of the flood provoked by an earthquake in California is higher. The researchers concluded that since an earthquake causing a flood in California is more specific and easier to imagine (versus a flood in an ambiguous area like all of North America), people are more likely to believe its probability. The same pattern was observed in other problems.

The study highlights our tendency to believe that the easier that something is to recall, the more frequent it must happen. This tendency, known as the availability heuristic, is a common bias we fall into as a way for our brains to make conclusions with little mental effort or strain based on evidence that is instantly available into our mind. It can also heavily influence our decision-making abilities and prevent us from seeing the bigger picture.

A 1976 study conducted at Stanford University presented participants with hypothetical situations and asked them to determine what percentage of people would make one of two choices. For example, one situation asked participants to determine what percentage of their peers would vote for a referendum that allocated large sums of money towards a revived space program with the goal of manned and unmanned exploration of the moon and planets nearest Earth. Some additional context was given, explaining that supporters would argue that this would provide jobs, spur technology, and promote national pride and unity. In contrast, opponents argue that a space program will increase taxes or else drain money from important domestic priorities while not achieving the goals of the program.

Once the participants provided their estimate, they were asked to disclose what they would do given the situation and fill out two questionnaires about the personality traits of those who would make each of the two choices. Researchers concluded that the participants not only expected that most of their peers would make the same choices they did but assumed those that opted for the opposite choice had extreme personality traits.

The study highlights our tendency to assume that our personal qualities, characteristics, beliefs, and actions are relatively widespread while alternate points of view are rare, deviant, and more extreme. This bias, known in psychology as the false-consensus effect, can skew our thinking and negatively influence design decisions.