Decision-making based on limited information is a common occurrence. Whether it is the possibility of a cheaper product elsewhere or the unknown qualifications of election candidates, people are regularly forced to make a decision under ignorance or uncertainty. In such situations, information about certain events is unavailable or too costly to acquire and people rely on subjective probability allocation to guide decision-making processes. This allocation seems to result in what is known as ignorance priors, i.e., decision-makers assigning equal probabilities to each possible outcome within a given set. How events are grouped or partitioned is often subjective and may influence probability judgments and subsequent decisions. In such cases, the way the choices within a choice set are presented may shape the perceived likelihood of different outcomes. Understanding the impact of partitioning on probability estimation is crucial for both psychological and economic theories of judgment and decision-making.

In the current work, Ding and Feldman (2025) conducted a replication study of one of the foundational works on the topic: Fox and Rottenstreich (2003). In the original work, the authors provided exploratory evidence indicating that the framing of a situation affects the way individuals perceive probabilities of possible outcomes. They showed that people assigned uniform probabilities to sets of events described in a problem, such that the way the events are described partly determined people’s partitioning of those events and evaluations of the probabilities of the possible outcomes. Additionally, this partitioned framing affected judgments both under conditions of ignorance (where individuals have no information and rely solely on uniform probability assignments) and uncertainty (where individuals have some information but still rely on heuristics influenced by partitioning). This suggests that priors resulting from the inference of available evidence are sometimes partly contaminated by partitioning bias, affecting both uninformed and partially informed decision-making processes. As a consequence, the partitioning of events into different subsets might lead to varying evaluations of a single situation, resulting in inconsistencies and poorly calibrated probability assessments.

Ding and Feldman (2025) conducted a replication work on Studies 1a, 1b, 3, and 4 from Fox and Rottenstreich (2003). Their close replication relies on original data (US participants, Prolific, N=603) with a large statistical power (>95%). The replication aimed to assess whether the partitioned framing affects prior formation under ignorance (Studies 1a, 1b, and 4) and uncertainty (Study 3). The authors also proposed an extension examining estimations of complementary events contrasting estimations of the probabilities of the events happening versus the probabilities of the events not happening.

Overall, the authors successfully replicated the original study based on their pre-registered evaluation criteria, finding support for partition dependence for most scenarios under scrutiny, yet with weaker effect sizes than the original studies. Out of the eleven Cohen’s h estimated by the replication study, one is consistent with the original study’s estimate (i.e., the original point estimate lies within the confidence interval of the replication), seven go in the same direction but are smaller (i.e., same sign for the estimated effect but the original point estimate is outside the CI of the replication), and two are not statistically different from zero (i.e., the CI of the replication includes zero).