![]() Here we will also propose a third, α -debt, which does not maintain a constant false positive rate but allows it to grow controllably. There are several proposed solutions to address multiple sequential analyses, namely α -spending and α -investing procedures ( Aharoni and Rosset, 2014 Foster and Stine, 2008), which strictly control false positive rate. Simultaneous procedures correct for all tests at once, while sequential procedures correct for the latest in a non-simultaneous series of tests. However, as we discuss in this article, when performing hypothesis testing it is important to take into account all of the statistical tests that have been performed on the datasets.Ī distinction can be made between simultaneous and sequential correction procedures when correcting for multiple tests. At present, researchers reusing datasets tend to correct for the number of statistical tests that they perform on the datasets. However, researchers re-analyzing these datasets will need to exercise caution if they intend to perform hypothesis testing. ![]() ![]() The availability of open datasets will increase over time as funders mandate and reward data sharing and other open research practices ( McKiernan et al., 2016). ![]() The ability to explore pre-existing datasets in new ways should make research more efficient and has the potential to yield new discoveries ( Weston et al., 2019). Making data open will allow other researchers to both reproduce published analyses and ask new questions of existing datasets ( Molloy, 2011 Pisani et al., 2016). In recent years, there has been a push to make the scientific datasets associated with published papers openly available to other researchers ( Nosek et al., 2015). ![]()
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