<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>

COPY SCRIPT

For further information contact us at helpdesk@openaire.eu

Why statistical testing and confidence intervals should not be used in comparative life cycle assessments based on Monte Carlo simulations

descriptionPublicationkeyboard_double_arrow_right Article , Journal 06 Oct 2020 Sweden, Sweden Publisher:Springer Science and Business Media LLCJournal:The International Journal of Life Cycle Assessment, volume 25, pages 2,101-2,105 (issn: 0948-3349, eissn: 1614-7502,

Authors: Claudia von Brömssen; Elin Röös;

doi: 10.1007/s11367-020-01827-4

Why statistical testing and confidence intervals should not be used in comparative life cycle assessments based on Monte Carlo simulations

- Summary
- Subjects
- Metrics

Abstract

AbstractIn the last years, it has been suggested to use statistical inferential methods, such as hypothesis testing or confidence intervals, to compare different products, services, or systems within comparative life cycle assessments based on Monte Carlo simulation results. However, the use of statistical inferential methods in such settings is fundamentally incorrect and should not be continued. In this article, we explain why and look closer at some related topics.

Countries

Sweden, Sweden

Related Organizations

Swedish University of Agricultural Sciences
Sweden

Keywords

10106 Probability Theory and Statistics

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	29
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%