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@ARTICLE{Piper:140670,
      author       = {Piper, Sophie K and Grittner, Ulrike and Rex, Andre and
                      Riedel, Nico and Fischer, Felix and Nadon, Robert and
                      Siegerink, Bob and Dirnagl, Ulrich},
      title        = {{E}xact replication: {F}oundation of science or game of
                      chance?},
      journal      = {PLoS biology},
      volume       = {17},
      number       = {4},
      issn         = {1545-7885},
      address      = {Lawrence, KS},
      publisher    = {PLoS},
      reportid     = {DZNE-2020-06992},
      pages        = {e3000188},
      year         = {2019},
      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.},
      keywords     = {Animals / Bayes Theorem / Biomedical Research: methods /
                      Biomedical Research: statistics $\&$ numerical data / Data
                      Interpretation, Statistical / Humans / Models, Statistical /
                      Probability / Publications / Reproducibility of Results /
                      Research Design: statistics $\&$ numerical data / Sample
                      Size},
      cin          = {AG Dirnagl},
      ddc          = {610},
      cid          = {I:(DE-2719)1810002},
      pnm          = {344 - Clinical and Health Care Research (POF3-344)},
      pid          = {G:(DE-HGF)POF3-344},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30964856},
      pmc          = {pmc:PMC6456162},
      doi          = {10.1371/journal.pbio.3000188},
      url          = {https://pub.dzne.de/record/140670},
}