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Exact replication: Foundation of science or game of chance?

Author

Listed:
  • Sophie K Piper
  • Ulrike Grittner
  • Andre Rex
  • Nico Riedel
  • Felix Fischer
  • Robert Nadon
  • Bob Siegerink
  • Ulrich Dirnagl

Abstract

The need for replication of initial results has been rediscovered only recently in many fields of research. In preclinical biomedical research, it is common practice to conduct exact replications with the same sample sizes as those used in the initial experiments. Such replication attempts, however, have lower probability of replication than is generally appreciated. Indeed, in the common scenario of an effect just reaching statistical significance, the statistical power of the replication experiment assuming the same effect size is approximately 50%—in essence, a coin toss. Accordingly, we use the provocative analogy of “replicating” a neuroprotective drug animal study with a coin flip to highlight the need for larger sample sizes in replication experiments. Additionally, we provide detailed background for the probability of obtaining a significant p value in a replication experiment and discuss the variability of p values as well as pitfalls of simple binary significance testing in both initial preclinical experiments and replication studies with small sample sizes. We conclude that power analysis for determining the sample size for a replication study is obligatory within the currently dominant hypothesis testing framework. Moreover, publications should include effect size point estimates and corresponding measures of precision, e.g., confidence intervals, to allow readers to assess the magnitude and direction of reported effects and to potentially combine the results of initial and replication study later through Bayesian or meta-analytic approaches.Using a coin toss to ‘replicate’ a neuroprotective effect of valproic acid, this study highlights the fact that exact replications of biomedical experiments are usually underpowered, often with power of approximately 50%.

Suggested Citation

  • Sophie K Piper & Ulrike Grittner & Andre Rex & Nico Riedel & Felix Fischer & Robert Nadon & Bob Siegerink & Ulrich Dirnagl, 2019. "Exact replication: Foundation of science or game of chance?," PLOS Biology, Public Library of Science, vol. 17(4), pages 1-9, April.
  • Handle: RePEc:plo:pbio00:3000188
    DOI: 10.1371/journal.pbio.3000188
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    References listed on IDEAS

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    1. Regina Nuzzo, 2014. "Scientific method: Statistical errors," Nature, Nature, vol. 506(7487), pages 150-152, February.
    2. Konrad Neumann & Ulrike Grittner & Sophie K Piper & Andre Rex & Oscar Florez-Vargas & George Karystianis & Alice Schneider & Ian Wellwood & Bob Siegerink & John P A Ioannidis & Jonathan Kimmelman & Ul, 2017. "Increasing efficiency of preclinical research by group sequential designs," PLOS Biology, Public Library of Science, vol. 15(3), pages 1-9, March.
    3. Colin F. Camerer & Anna Dreber & Felix Holzmeister & Teck-Hua Ho & Jürgen Huber & Magnus Johannesson & Michael Kirchler & Gideon Nave & Brian A. Nosek & Thomas Pfeiffer & Adam Altmejd & Nick Buttrick , 2018. "Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015," Nature Human Behaviour, Nature, vol. 2(9), pages 637-644, September.
    4. C. Glenn Begley & Lee M. Ellis, 2012. "Raise standards for preclinical cancer research," Nature, Nature, vol. 483(7391), pages 531-533, March.
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