ArticlePloS one2018
Visualizing the Bayesian 2-test case: The effect of tree diagrams on medical decision making.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 30 citations in OpenAlex.
- Risk expression using likelihood ratios and natural frequencies in Bayesian inference tasks-a preregistered randomized-controlled crossover trial.BMC medical education · 2025Trial
- Natural frequency tree- versus conditional probability formula-based training for medical students' estimation of screening test predictive values: a randomized controlled trial.BMC medical education · 2024Trial
- Presentation format influences the strength of causal illusions.Memory & cognition · 2025Article
- How general is the natural frequency effect? The case of joint probabilities.Frontiers in psychology · 2024Article
- Article
- Natural frequency trees improve diagnostic efficiency in Bayesian reasoning.Advances in health sciences education : theory and practice · 2021Article
- Tversky and Kahneman's Cognitive Illusions: Who Can Solve Them, and Why?Frontiers in psychology · 2021Article
- A New Visualization for Probabilistic Situations Containing Two Binary Events: The Frequency Net.Frontiers in psychology · 2020Article
- Complexity of clinical cases in simulated learning environments: proposal for a scoring system.GMS journal for medical education · 2019Article
- Evidencing How Experience and Problem Format Affect Probabilistic Reasoning Through Interaction Analysis.Frontiers in psychology · 2019Article
- An Eye-Tracking Study of Statistical Reasoning With Tree Diagrams and 2 × 2 Tables.Frontiers in psychology · 2019Article
- Why Can Only 24% Solve Bayesian Reasoning Problems in Natural Frequencies: Frequency Phobia in Spite of Probability Blindness.Frontiers in psychology · 2018Article
- Determining Required Teaching Methods in Distance Undergraduate Medical Education Based on the Non-University Learning Methods Medical Students Used During Covid-19: An Exploratory Factor Analysis.Current health sciences journalArticle
- Improving Diagnostic Efficiency with Frequency Double-Trees and Frequency Nets in Bayesian Reasoning.MDM policy & practiceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In medicine, diagnoses based on medical test results are probabilistic by nature. Unfortunately, cognitive illusions regarding the statistical meaning of test results are well documented among patients, medical students, and even physicians. There are two effective strategies that can foster insight into what is known as Bayesian reasoning situations: (1) translating the statistical information on the prevalence of a disease and the sensitivity and the false-alarm rate of a specific test for that disease from probabilities into natural frequencies, and (2) illustrating the statistical information with tree diagrams, for instance, or with other pictorial representation. So far, such strategies have only been empirically tested in combination for "1-test cases", where one binary hypothesis ("disease" vs. "no disease") has to be diagnosed based on one binary test result ("positive" vs. "negative"). However, in reality, often more than one medical test is conducted to derive a diagnosis. In two studies, we examined a total of 388 medical students from the University of Regensburg (Germany) with medical "2-test scenarios". Each student had to work on two problems: diagnosing breast cancer with mammography and sonography test results, and diagnosing HIV infection with the ELISA and Western Blot tests. In Study 1 (N = 190 participants), we systematically varied the presentation of statistical information ("only textual information" vs. "only tree diagram" vs. "text and tree diagram in combination"), whereas in Study 2 (N = 198 participants), we varied the kinds of tree diagrams ("complete tree" vs. "highlighted tree" vs. "pruned tree"). All versions were implemented in probability format (including probability trees) and in natural frequency format (including frequency trees). We found that natural frequency trees, especially when the question-related branches were highlighted, improved performance, but that none of the corresponding probabilistic visualizations did.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.