F
Fiona Fidler
Researcher at University of Melbourne
Publications - 93
Citations - 8599
Fiona Fidler is an academic researcher from University of Melbourne. The author has contributed to research in topics: Expert elicitation & Estimation statistics. The author has an hindex of 32, co-authored 88 publications receiving 6907 citations. Previous affiliations of Fiona Fidler include RMIT University & La Trobe University.
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The New Stats: Attitudes for the Twenty-First Century
TL;DR: In this article, the authors present the vanguard of research methods for the 21st century and provide advice for graduate students and researchers who want a comprehensive, authoritative resource for practical and sound advice.
Journal ArticleDOI
Rates and predictors of data and code sharing in the medical and health sciences: Protocol for a systematic review and individual participant data meta-analysis.
Daniel G. Hamilton,Hannah Fraser,Fiona Fidler,Steve McDonald,Anisa Rowhani-Farid,Kyungwan Hong,Matthew J. Page +6 more
TL;DR: This systematic review aims to synthesise the findings of medical and health science studies that have empirically investigated the prevalence of data or code sharing, or both, to provide some insight into how often research data and code are shared publicly and privately, how this has changed over time, and how effective some measures such as the institution of data sharing policies and data availability statements have been in motivating researchers to share their underlying data andcode.
Book ChapterDOI
Statistical significance, result worthiness and evidence: What lessons are there for giftedness education in other disciplines?
Journal ArticleDOI
Yes, but Don’t Underestimate Estimation: Reply to Morey, Rouder, Verhagen, and Wagenmakers (2014)
Fiona Fidler,Geoff Cumming +1 more
TL;DR: In their view, Morey et al. made appropriate use of estimation to evaluate the model and guide its further development, and were critical of the arbitrariness of basing the conclusion on 11 of 15 correct predictions and also of the neglect of results expected if the model under test were false.