Evidence map›Paper›PMID 37908812›Full record

ArticleFrontiers in psychology2023

Measuring people's covariational reasoning in Bayesian situations.

Nicole Steib, Stefan Krauss, Karin Binder, Theresa Büchter, Katharina Böcherer-Linder, Andreas Eichler, Markus Vogel

Abstract read
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In one paragraph

Article in Frontiers in psychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Nicole SteibMathematics Education, Faculty of Mathematics, University of Regensburg, Regensburg, Germany.
Stefan KraussMathematics Education, Faculty of Mathematics, University of Regensburg, Regensburg, Germany.
Karin BinderMathematics Education, Institute of Mathematics, Ludwig Maximilian University of Munich, Munich, Germany.
Theresa BüchterMathematics Education, Institute of Mathematics, University of Kassel, Kassel, Germany.
Katharina Böcherer-LinderMathematics Education, Institute of Mathematics, University of Freiburg, Freiburg, Germany.
Andreas EichlerMathematics Education, Institute of Mathematics, University of Kassel, Kassel, Germany.
Markus VogelMathematics Education, Institute of Mathematics, University of Education Heidelberg, Heidelberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Previous research on Bayesian reasoning has typically investigated people's ability to assess a posterior probability (i.e., a positive predictive value) based on prior knowledge (i.e., base rate, true-positive rate, and false-positive rate). In this article, we systematically examine the extent to which people understand the effects of changes in the three input probabilities on the positive predictive value, that is,

Indexed as

Bayesian reasoningcovariational reasoningdouble-treenatural frequenciesunit square

Identifiers

PMID37908812
PMCPMC10614641

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.