Some of the main questions that psychologists want to understand are “why do we think the way we do?”and“how do we reach conclusions?” One of the ways we reach conclusions is through inductive reasoning. Scientific observations typically use this type of reasoning. For inductive reasoning, observations are made, data are collected, and conclusions are drawn from the data. In order for the reasoning to be valid, the evidentiary support should be high. “The logic should make it likely (as a matter of logic) that as evidence accumulates, the total body of true evidence claims will eventually come to indicate, via the logic’s measure of support, that false hypotheses are probably false and that true hypotheses are probably true (stanford.edu). Inductive reasoning is also used in everyday life when we make predictions of what will happen based on past events. Heuristics are shortcuts for using past experience to make predictions, and are a form of inductive reasoning. However, heuristics, such as the availability heuristic and representative heuristic, can lead us to make errors in judgement.
The availability heuristic is a form of cognitive bias in which events that are remembered more easily are judged as being more probable than those remembered less easily. In a study of the availability heuristic, two researchers (Khaneman and Tversky) asked participants which was more common: words that begin with the letter k, or words that have k as the third letter. The participants could think of more examples of words starting with a k and were more likely to say that there are more words in that category. However, there are twice as many words that have k as the third letter than words that start with k (simplypsychology.org). This would be an example of an availability heuristic. Based on the way we categorize words and letters, it is easier to think of words starting with a letter than words that have a specific letter (k) in the third position. Therefore, participants assume they are more common.
Another example of an availability heuristic would be in self-evaluating assertiveness. In a study by Schwarz et al. (dornsife.usc.edu), researchers investigated how coming up with a few or many examples of assertiveness would impact self-evaluation of assertiveness. They divided the participants into 2 groups. The first group only had to produce 6 examples of assertive behavior, while the second group had to produce 12 examples of assertive behavior. Both groups then had to rank their level of assertiveness on a scale of 1-10. The group that had to recall 6 examples of assertive behavior rated their assertiveness higher (M=6.3) than the group that had to recall 12 examples of assertive behavior (M=5.2). The reason for this difference is that participants who only had to come up with 6 examples of assertive behavior found the task easier, and therefore believed themselves to be more assertive. Those who had to come up with 12 examples of assertive behaviors had a more difficult time finding such a large number of examples, and therefore believed themselves to be less assertive since 12 examples were not readily available.
Representativeness heuristic can be used when judging the probability that object/event A is a member of class B based on how well the properties of A resemble the properties of class B. In general, the rules we as individuals should follow would be to use base rate information (data and statistics) when provided to make judgements. However, we seem to use descriptive information instead and disregard base rate data, which can lead to inaccurate judgement. Here is one problem to examine: Bob is an opera fan who enjoys touring art museums when on holiday. Growing up, he enjoyed playing chess with family members and friends. Which situation is more likely? (A) Bob plays trumpet for a major symphony orchestra, or (B) Bob is a farmer (behavioraleconomics.com)? Most people would answer that A is more likely. However, this would be making an assumption based on descriptive information only. Yes, Bob’s characteristics resemble that of someone who would be in an orchestra. However, the percentage of the world population of farmers (26.7%) is much greater than the percentage population of musicians. It is statistically more likely that Bob is a farmer given the base rate information that a greater percentage of the population is made up of farmers.
Another representativeness heuristic can be seen in “The Linda Problem” (Khaneman and Tversky, 1983). Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations. Which is more probable? (A) Linda is a bank teller. (B) Linda is a bank teller and is active in the feminist movement (psychologytoday.com). More than 80 percent of participants chose option B, including expert statisticians. However, the probability of two events occurring in conjunction is always less than (or equal to) the probability of either event occurring alone. Even though logically option B is less likely to occur, participants pick it because it is associated with the descriptive information of what Linda did in college. The set of “all bankers” includes the subset of “bankers who are feminists.” It is impossible for a subset to contain more members than the set it comes from. Therefore, it is impossible for the probability that Linda is a banker and a feminist to be larger than the probability of being a banker.
In diving into reasoning, we can attempt to understand how bias and judgement can be flawed and try to make fewer errors. In the future, we should aim to avoid our cognitive biases and use more accurate statistical analyses when coming to conclusions.