Updated: Jan 24
Conjunction fallacy is a well-known judgmental bias that shows that people erroneously believe that events described in more detail are more probable than those that are described in less detail. Surprisingly, this effect has been found in expert attorneys' and judges. There is strong scientific evidence that lawyers are highly susceptible to bias in judging the likelihood of potential trial outcomes.
The conjunction fallacy originates from Amos Tversky and Daniel Kahneman. Tversky and Kahneman presented profiles to participants describing various hypothetical persons. In one of their most famous examples, Linda was presented as an intelligent and outspoken person who majored in philosophy. She was deeply concerned with discrimination and social justice issues as a student. The participants were then asked to rank order several statements about Linda regarding how likely they considered them to be. The critical statements were that Linda is a bank teller (option 1; BT) and that Linda is a bank teller and feminist (option 2; BT&F). Tversky and Kahneman reported results that indicated that the majority of the participants chose option 2.
However, the finding that Prob (BT&F) > Prob(BT) is not possible since the probability of two events occurring together (in "conjunction") is always less than or equal to the probability of either one occurring alone.
In a series of studies, Fox & Birke (2002) demonstrated that a single hypothesis is judged to be more likely when it is described as an explicit disjunction of component hypotheses or when these components are judged sequentially by the same individual. The researchers provided evidence that unpacking an event into a more detailed description yields higher judged probabilities. They recruited a sample of California attorneys with varied civil practices (N=97) and asked them to consider the antitrust case U.S. v. Microsoft. At the time of the study, a federal judge ruled on splitting Microsoft into two separate entities. Even though Microsoft indicated an intention to appeal, it was an open question whether the case would go to the Federal Court of Appeals or directly to the U.S. Supreme Court. Approximately half (n = 50) of the participants were asked to judge the probability of the hypotheses that the case would “go directly to the Supreme Court.” The remaining attorneys (n = 47) were asked to judge the probability of the hypotheses that the case would “go directly to the Supreme Court and be affirmed, reversed, or modified.” As elaborated above, the standard conditional probability theorem says that the judged probability of the latter hypothesis is the same as the judged probability of the former hypothesis because the latter hypothesis merely elaborates the former.
Nevertheless, the latter elaborated version received a higher median judged probability than did the unelaborated version. This result proves that lawyers judge an event's probability to be higher when it is unpacked into an explicit disjunction (or separate evaluation) of constituent events.
Judged probabilities are essential because they serve as a currency of communication between lawyers and their clients. A lawyer's prediction of the outcome of a trial plays a crucial role in many vital litigants' decisions, such as accepting or rejecting a settlement offer. In conclusion, the lawyers must expunge the conjunction fallacy from their judgments to compensate for the potential bias when forecasting outcomes.
Cheat sheet for busy lawyers
Forecasting Trial Outcomes: Lawyers Assign Higher Probability to Possibilities That Are Described in Greater Detail
Craig R. Fox and Richard Birke
Year of publication
Theoretical/ Conceptual Framework
People judge the probability of an event to be higher when the event is described as a disjunction of constituent events or when they judge constituent events separately. The study test if attorneys are indeed susceptible to bias in forecasting trial outcomes, consistent with this theory.
Research Question(s)/ Hypotheses
The study test the prediction of implicit subadditivity: unpacking the description of an event into a more detailed description of disjoint components will generally yield a higher judged probability.
Sample of California attorneys with varied civil practices (N = 97) were asked to consider the antitrust case U.S. v. Microsoft and judge the probability of that the case would “go directly to the Supreme Court.” (simple version) or the case would “go directly to the Supreme Court and be affirmed, reversed, or modified.” (elaborated version)
The elaborated version of the outcome received a higher judged probability than the unelaborated version.