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.
Papers
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Lessons learned from statistical reform efforts in other disciplines
Fiona Fidler,Geoff Cumming +1 more
TL;DR: In this paper, the authors discuss how reform has progressed or not progressed in psychology, medicine, and ecology and describe case studies of attempts by pioneering journal editors to change statistical practices.
Interval estimates for statistical communication : problems and possible solutions
Fiona Fidler,Geoffrey Cumming +1 more
TL;DR: Better guidelines, or ‘rules of eye’, and improved graphical presentations are suggested to assist with confidence interval presentation and interpretation, to facilitate conceptual change in thinking not only about interval estimates themselves, but also the often misunderstood concept of statistical significance.
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The REPRISE project: protocol for an evaluation of REProducibility and Replicability In Syntheses of Evidence
Matthew J. Page,David Moher,David Moher,Fiona Fidler,Julian P T Higgins,Sue E. Brennan,Neal R. Haddaway,Daniel G. Hamilton,Raju Kanukula,Sathya Karunananthan,Lara J Maxwell,Steve McDonald,Shinichi Nakagawa,David Nunan,Peter Tugwell,Vivian Welch,Joanne E. McKenzie +16 more
TL;DR: The REPRISE project takes a systematic approach to determine how reliable systematic reviews of interventions are and aims to explore various aspects relating to the transparency, reproducibility and replicability of several components of systematic reviews with meta-analysis of the effects of health, social, behavioural and educational interventions.
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The Epistemic Importance of Establishing the Absence of an Effect
TL;DR: The authors provide a brief outline of the four main models for establishing epistemic justification in science and explain the essential role that establishing null results plays in each of them, and argue that scientists could do worse than follow Popper's advice about bold conjectures and risky tests in establishing the absence (or presence) of effects.
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Still Much to Learn About Confidence Intervals: Reply to Rouder and Morey (2005)
TL;DR: Recommendations reinforce the need to report standard deviations, but do not justify noninterpretation of CIs, and support research to develop and evaluate better guidelines for use and interpretation of CI.