Journal Article

DZNE-2020-06992

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Exact replication: Foundation of science or game of chance?

Piper, S. K. ; Grittner, U. ; Rex, A. ; Riedel, N. ; Fischer, F. ; Nadon, R. ; Siegerink, B. ; Dirnagl, U. (Last author)DZNE*

2019
PLoS Lawrence, KS

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Please use a persistent id in citations: doi:10.1371/journal.pbio.3000188

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.

Keyword(s): Animals (MeSH) ; Bayes Theorem (MeSH) ; Biomedical Research: methods (MeSH) ; Biomedical Research: statistics & numerical data (MeSH) ; Data Interpretation, Statistical (MeSH) ; Humans (MeSH) ; Models, Statistical (MeSH) ; Probability (MeSH) ; Publications (MeSH) ; Reproducibility of Results (MeSH) ; Research Design: statistics & numerical data (MeSH) ; Sample Size (MeSH)

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Contributing Institute(s):

1. Vascular Pathology (AG Dirnagl)

Research Program(s):

1. 344 - Clinical and Health Care Research (POF3-344) (POF3-344)

Appears in the scientific report 2019

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Database coverage:
; ; ; ; BIOSIS Previews ; Clarivate Analytics Master Journal List ; Current Contents - Agriculture, Biology and Environmental Sciences ; Current Contents - Life Sciences ; DOAJ Seal ; Ebsco Academic Search ; IF >= 5 ; JCR ; SCOPUS ; Web of Science Core Collection ; Zoological Record

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