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A framework estimating the minimum sample size and margin of error for maritime quantitative risk analysis
Abstract
The average accident frequency is essential for quantitative risk analysis and is conventionally estimated from accident statistics. This paper has systematically synthesised the knowledge on statistical errors and offered the missing instructions, a framework, for determining the minimum sample size and the margin of error (MOE) when calculating the average accident frequency from an accident database at hand. We have applied this framework to representative accident datasets in the maritime domain and presented the revealing results that can already be used in QRAs based on these datasets. The findings are useful to both QRA analysts and policy makers. Interestingly, the framework application has revealed that the determined minimum sample sizes would exceed the datasets available in existing maritime casualty databases by decades, requiring at least 10% MOE to be factored into pertinent QRAs. By the same token, the earlier notable QRAs (developed as part of formal safety assessments in support of rule making) had to consider the MOE of over 30%, given the sample sizes used, likely shifting the conclusions they arrived at. Other findings of the application have shown that the average accident frequencies for large passenger ships have remained constant over the past 40 years.
Citations
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Authors (3)
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Romanas Puisa, Dr
- RINA, UK
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.ress.2023.109221
- License
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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RELIABILITY ENGINEERING & SYSTEM SAFETY
no. 235,
ISSN: 0951-8320 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Puisa, R., Krata P., Montewka J.: A framework estimating the minimum sample size and margin of error for maritime quantitative risk analysis// RELIABILITY ENGINEERING & SYSTEM SAFETY -Vol. 235, (2023),
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.ress.2023.109221
- Sources of funding:
-
- IDUB
- Verified by:
- Gdańsk University of Technology
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