Explanations of Statistical Concepts: Can they Penetrate the Haze of Bayes?

P. E. Politser

doi:10.1055/s-0038-1636688

Methods of Information in Medicine, Table of Contents

Methods Inf Med 1984; 23(02): 99-108
DOI: 10.1055/s-0038-1636688

Original Article

Schattauer GmbH

Explanations of Statistical Concepts: Can they Penetrate the Haze of Bayes?

Erklärungen statistischer Begriffe: Können sie den Bayes’schen Dunstschleier durchdringen?

Authors

P. E. Politser^*

¹(From the Department of Biometry, Medical School, Case Western Reserve University, Cleveland, Ohio)

Abstract

Summary

This experiment tested the ability of logical explanations to promote the use of statistical concepts. Sixty physicians in four experimental groups, matched for level of medical statistical training, were given a questionnaire that assessed their ability to rank the diagnosticity of various diagnostic test results. Each question was designed to test physician understanding of the impact of a particular statistical parameter, e.g., sensitivity and specificity, test-retest reliability. Logical explanations of the sources of test error increased the percentage of answers judged to be correct on the basis of published data. This was confirmed for a variety of questions. They tended to be less useful, however, when physicians expressed misinterpretations of statistical concepts or test patterns were not correctly perceived. The implications of these findings for medical education and for the design of computer-based explanation systems are clarified.

In diesem Experiment wurde die Fähigkeit logischer Erklärungen zur Förderung der Anwendung statistischer Begriffe untersucht. Sechzig Ärzten mit gleichem Niveau medizinisch-statistischer Ausbildung in vier Studiengruppen wurde ein Fragebogen gegeben, der ihre Fähigkeit zur Wertung der diagnostischen Macht verschiedener diagnostischer Testergebnisse einschätzen sollte. Jede Frage war so formuliert, daß sich aus der Antwort ergeben sollte, inwieweit der Arzt die Wirkung eines bestimmten statistischen Parameters (z.B. Sensitivität und Spezifizität, Test-Retest-Zuverlässigkeit) verstehen konnte. Logische Erklärungen der Quellen von Testfehlern erhöhten den Prozentsatz der aufgrund veröffentlichter Daten für richtig erachteten Antworten. Dies wurde für eine Vielfalt von Fragen bestätigt. Sie erwiesen sich jedoch als weniger nützlich, wenn Ärzte Fehlinterpretationen statistischer Begriffe zum Ausdruck brachten oder wenn Testmuster nicht richtig erkannt wurden. Die Folgerungen dieser Feststellungen für die ärztliche Ausbildung und für die Planung computerunterstützter Erklärungssysteme werden geklärt.

Key-Words:

Diagnostic Tests - Decision-making - Psychological Studies of Clinical Judgement - Computer-based Consultation Systems - Artificial Intelligence

Schlüssel-Wörter:

Diagnostische Tests - Entscheidungsfindung - psychologische Studien über klinische Beurteilung - computerunterstützte Konsultationssysteme - künstliche Intelligenz

Full Text

References

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