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Autores principales: Hess, Stephane, Daly, Andrew, Bliemer, Michiel, Guevara, Angelo, Daziano, Ricardo, Dekker, Thijs
Formato: Preprint
Publicado: 2025
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Acceso en línea:https://arxiv.org/abs/2506.05996
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author Hess, Stephane
Daly, Andrew
Bliemer, Michiel
Guevara, Angelo
Daziano, Ricardo
Dekker, Thijs
author_facet Hess, Stephane
Daly, Andrew
Bliemer, Michiel
Guevara, Angelo
Daziano, Ricardo
Dekker, Thijs
contents This paper offers a commentary on the use of notions of statistical significance in choice modelling. We review the reasons for uncertainty in parameter estimates, provide a precise discussion on the computation of measures of uncertainty and confidence intervals, and discuss the use of statistical tests. We argue that, as in many other areas of science, there is an over-reliance on 95\% confidence levels, and misunderstandings of the meaning of significance. We also observe a lack of precision in the reporting of measures of uncertainty in many studies, especially when using $p$-values and even more so with \emph{star} measures. The paper also stresses the importance of considering behavioural or policy significance in addition to statistical significance. Finally, we stress a number of points that are specific to choice modelling and which require special attention, notably in relation to derived measures such as willingness-to-pay, the treatment of random heterogeneity, and the use of repeated choice data.
format Preprint
id arxiv_https___arxiv_org_abs_2506_05996
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Statistical significance in choice modelling: computation, usage and reporting
Hess, Stephane
Daly, Andrew
Bliemer, Michiel
Guevara, Angelo
Daziano, Ricardo
Dekker, Thijs
Econometrics
This paper offers a commentary on the use of notions of statistical significance in choice modelling. We review the reasons for uncertainty in parameter estimates, provide a precise discussion on the computation of measures of uncertainty and confidence intervals, and discuss the use of statistical tests. We argue that, as in many other areas of science, there is an over-reliance on 95\% confidence levels, and misunderstandings of the meaning of significance. We also observe a lack of precision in the reporting of measures of uncertainty in many studies, especially when using $p$-values and even more so with \emph{star} measures. The paper also stresses the importance of considering behavioural or policy significance in addition to statistical significance. Finally, we stress a number of points that are specific to choice modelling and which require special attention, notably in relation to derived measures such as willingness-to-pay, the treatment of random heterogeneity, and the use of repeated choice data.
title Statistical significance in choice modelling: computation, usage and reporting
topic Econometrics
url https://arxiv.org/abs/2506.05996