Exact replication: Foundation of science or game of chance?
| Autor: | Bob Siegerink, Felix Fischer, Robert Nadon, André Rex, Nico Riedel, Sophie K. Piper, Ulrike Grittner, Ulrich Dirnagl |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Biomedical Research QH301-705.5 statistics & numerical data [Biomedical Research] Biology General Biochemistry Genetics and Molecular Biology Statistical power 03 medical and health sciences Bayes' theorem 0302 clinical medicine Replication (statistics) Statistics Animals Humans Point estimation p-value ddc:610 Biology (General) Statistical hypothesis testing Probability Models Statistical General Immunology and Microbiology Coin flipping General Neuroscience Publications Reproducibility of Results Bayes Theorem statistics & numerical data [Research Design] 030104 developmental biology Research Design Sample size determination Data Interpretation Statistical Sample Size methods [Biomedical Research] General Agricultural and Biological Sciences 030217 neurology & neurosurgery |
| Zdroj: | PLoS biology 17(4), e3000188 (2019). doi:10.1371/journal.pbio.3000188 PLoS Biology, Vol 17, Iss 4, p e3000188 (2019) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|