---
_id: '59434'
abstract:
- lang: eng
text: ' Background. Medical students often have problems with Bayesian reasoning
situations. Representing statistical information as natural frequencies (instead
of probabilities) and visualizing them (e.g., with double-trees or net diagrams)
leads to higher accuracy in solving these tasks. However, double-trees and net
diagrams (which already contain the correct solution of the task, so that the
solution could be read of the diagrams) have not yet been studied in medical education.
This study examined the influence of information format (probabilities v. frequencies)
and visualization (double-tree v. net diagram) on the accuracy and speed of Bayesian
judgments. Methods. A total of 142 medical students at different university medical
schools (Munich, Kiel, Goettingen, Erlangen, Nuremberg, Berlin, Regensburg) in
Germany predicted posterior probabilities in 4 different medical Bayesian reasoning
tasks, resulting in a 3-factorial 2 × 2 × 4 design. The diagnostic efficiency
for the different versions was represented as the median time divided by the percentage
of correct inferences. Results. Frequency visualizations led to a significantly
higher accuracy and faster judgments than did probability visualizations. Participants
solved 80% of the tasks correctly in the frequency double-tree and the frequency
net diagram. Visualizations with probabilities also led to relatively high performance
rates: 73% in the probability double-tree and 70% in the probability net diagram.
The median time for a correct inference was fastest with the frequency double
tree (2:08 min) followed by the frequency net diagram and the probability double-tree
(both 2:26 min) and probability net diagram (2:33 min). The type of visualization
did not result in a significant difference. Discussion. Frequency double-trees
and frequency net diagrams help answer Bayesian tasks more accurately and also
more quickly than the respective probability visualizations. Surprisingly, the
effect of information format (probabilities v. frequencies) on performance was
higher in previous studies: medical students seem also quite capable of identifying
the correct solution to the Bayesian task, among other probabilities in the probability
visualizations. Highlights
Frequency double-trees and frequency nets help answer Bayesian tasks not only
more accurately but also more quickly than the respective probability visualizations.
In double-trees and net diagrams, the effect of the information format (probabilities
v. natural frequencies) on performance is remarkably lower in this high-performing
sample than that shown in previous studies. '
article_type: original
author:
- first_name: Alexandra K.
full_name: Kunzelmann, Alexandra K.
last_name: Kunzelmann
- first_name: Karin
full_name: Binder, Karin
id: '83381'
last_name: Binder
- first_name: Martin R.
full_name: Fischer, Martin R.
last_name: Fischer
- first_name: Martin
full_name: Reincke, Martin
last_name: Reincke
- first_name: Leah T.
full_name: Braun, Leah T.
last_name: Braun
- first_name: Ralf
full_name: Schmidmaier, Ralf
last_name: Schmidmaier
citation:
ama: Kunzelmann AK, Binder K, Fischer MR, Reincke M, Braun LT, Schmidmaier R. Improving
Diagnostic Efficiency with Frequency Double-Trees and Frequency Nets in Bayesian
Reasoning. MDM Policy & Practice. 2022;7(1). doi:10.1177/23814683221086623
apa: Kunzelmann, A. K., Binder, K., Fischer, M. R., Reincke, M., Braun, L. T., &
Schmidmaier, R. (2022). Improving Diagnostic Efficiency with Frequency Double-Trees
and Frequency Nets in Bayesian Reasoning. MDM Policy & Practice,
7(1). https://doi.org/10.1177/23814683221086623
bibtex: '@article{Kunzelmann_Binder_Fischer_Reincke_Braun_Schmidmaier_2022, title={Improving
Diagnostic Efficiency with Frequency Double-Trees and Frequency Nets in Bayesian
Reasoning}, volume={7}, DOI={10.1177/23814683221086623},
number={1}, journal={MDM Policy & Practice}, publisher={SAGE Publications},
author={Kunzelmann, Alexandra K. and Binder, Karin and Fischer, Martin R. and
Reincke, Martin and Braun, Leah T. and Schmidmaier, Ralf}, year={2022} }'
chicago: Kunzelmann, Alexandra K., Karin Binder, Martin R. Fischer, Martin Reincke,
Leah T. Braun, and Ralf Schmidmaier. “Improving Diagnostic Efficiency with Frequency
Double-Trees and Frequency Nets in Bayesian Reasoning.” MDM Policy &
Practice 7, no. 1 (2022). https://doi.org/10.1177/23814683221086623.
ieee: 'A. K. Kunzelmann, K. Binder, M. R. Fischer, M. Reincke, L. T. Braun, and
R. Schmidmaier, “Improving Diagnostic Efficiency with Frequency Double-Trees and
Frequency Nets in Bayesian Reasoning,” MDM Policy & Practice, vol.
7, no. 1, 2022, doi: 10.1177/23814683221086623.'
mla: Kunzelmann, Alexandra K., et al. “Improving Diagnostic Efficiency with Frequency
Double-Trees and Frequency Nets in Bayesian Reasoning.” MDM Policy &
Practice, vol. 7, no. 1, SAGE Publications, 2022, doi:10.1177/23814683221086623.
short: A.K. Kunzelmann, K. Binder, M.R. Fischer, M. Reincke, L.T. Braun, R. Schmidmaier,
MDM Policy & Practice 7 (2022).
date_created: 2025-04-08T11:33:53Z
date_updated: 2025-05-17T18:22:27Z
ddc:
- '370'
doi: 10.1177/23814683221086623
file:
- access_level: closed
content_type: application/pdf
creator: binder
date_created: 2025-05-17T18:22:12Z
date_updated: 2025-05-17T18:22:12Z
file_id: '59930'
file_name: Kunzelmann Binder Fischer Reincke Braun Schmidmaier_2022_Improving diagnostic
efficiency_MDM_PP.pdf
file_size: 1131713
relation: main_file
success: 1
file_date_updated: 2025-05-17T18:22:12Z
intvolume: ' 7'
issue: '1'
language:
- iso: eng
publication: MDM Policy & Practice
publication_identifier:
issn:
- 2381-4683
- 2381-4683
publication_status: published
publisher: SAGE Publications
status: public
title: Improving Diagnostic Efficiency with Frequency Double-Trees and Frequency Nets
in Bayesian Reasoning
type: journal_article
user_id: '83381'
volume: 7
year: '2022'
...