TY - JOUR
T1 - On de-bunking “Fake News” in the post-truth era
T2 - How to reduce statistical error in research
AU - Flyvbjerg, Bent
AU - Ansar, Atif
AU - Budzier, Alexander
AU - Buhl, Søren
AU - Cantarelli, Chantal
AU - Garbuio, Massimo
AU - Glenting, Carsten
AU - Holm, Mette Skamris
AU - Lovallo, Dan
AU - Molin, Eric
AU - Rønnest, Arne
AU - Stewart, Allison
AU - van Wee, Bert
N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2019
Y1 - 2019
N2 - The authors note with alarm that statistical noise caused by statistical incompetence is beginning to creep into research on cost overrun in public investment projects, contaminating research with work that does not meet basic standards of validity and reliability. The paper gives examples of such work and proposes three heuristics to root out the problem. First, researchers who are not statisticians, or do not have a strong background in statistics, should abstain from doing statistical analysis, and instead rely on more experienced colleagues, preferably professional statisticians. Second, journal referees should clearly state their level of statistical proficiency to journal editors, so these can set the right referee team. Finally, journal editors should make sure that at least one referee is capable of reviewing the statistical and methodological aspects of a paper. The work under review would have benefitted from observing these simple heuristics, as would any work based on statistical analysis.
AB - The authors note with alarm that statistical noise caused by statistical incompetence is beginning to creep into research on cost overrun in public investment projects, contaminating research with work that does not meet basic standards of validity and reliability. The paper gives examples of such work and proposes three heuristics to root out the problem. First, researchers who are not statisticians, or do not have a strong background in statistics, should abstain from doing statistical analysis, and instead rely on more experienced colleagues, preferably professional statisticians. Second, journal referees should clearly state their level of statistical proficiency to journal editors, so these can set the right referee team. Finally, journal editors should make sure that at least one referee is capable of reviewing the statistical and methodological aspects of a paper. The work under review would have benefitted from observing these simple heuristics, as would any work based on statistical analysis.
UR - http://www.scopus.com/inward/record.url?scp=85068384294&partnerID=8YFLogxK
U2 - 10.1016/j.tra.2019.06.011
DO - 10.1016/j.tra.2019.06.011
M3 - Article
AN - SCOPUS:85068384294
SN - 0965-8564
VL - 126
SP - 409
EP - 411
JO - Transportation Research Part A: Policy and Practice
JF - Transportation Research Part A: Policy and Practice
ER -