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Open AccessArticle

How Cognitive Biases Influence the Data Verification of Safety Indicators: A Case Study in Rail

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Safety and Security Science Group, Delft University of Technology, Jaffalaan 5, 2628 BX Delft, The Netherlands
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Cognitive Psychology Unit, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands
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TNO Leiden, Schipholweg 77-89, 2316 ZL Leiden, The Netherlands
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Author to whom correspondence should be addressed.
Safety 2019, 5(4), 69; https://doi.org/10.3390/safety5040069
Received: 20 July 2019 / Revised: 28 September 2019 / Accepted: 11 October 2019 / Published: 15 October 2019
(This article belongs to the Special Issue Occupational Health and Safety New Challenges for Industry)
The field of safety and incident prevention is becoming more and more data based. Data can help support decision making for a more productive and safer work environment, but only if the data can be, is and should be trusted. Especially with the advance of more data collection of varying quality, checking and judging the data is an increasingly complex task. Within such tasks, cognitive biases are likely to occur, causing analysists to overestimate the quality of the data and safety experts to base their decisions on data of insufficient quality. Cognitive biases describe generic error tendencies of persons, that arise because people tend to automatically rely on their fast information processing and decision making, rather than their slow, more effortful system. This article describes five biases that were identified in the verification of a safety indicator related to train driving. Suggestions are also given on how to formalize the verification process. If decision makers want correct conclusions, safety experts need good quality data. To make sure insufficient quality data is not used for decision making, a solid verification process needs to be put in place that matches the strengths and limits of human cognition. View Full-Text
Keywords: cognitive bias; safety indicator; verification; OHS management; safety data; incident prevention; human factors cognitive bias; safety indicator; verification; OHS management; safety data; incident prevention; human factors
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MDPI and ACS Style

Burggraaf, J.; Groeneweg, J.; Sillem, S.; van Gelder, P. How Cognitive Biases Influence the Data Verification of Safety Indicators: A Case Study in Rail. Safety 2019, 5, 69.

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