Am writing experimentalist's guide on Open Access for Mss. and data
But found conflicting advice
1. Null-hypothesis testing should not be conducted - ever.
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA Statement on p-Values: Context, Process, and Purpose. The American Statistician, 70(2), 129-133. doi:10.1080/00031305.2016.1154108
2. The rationale for the sample size should be given (e.g. an a priori power analysis)
Aczel, B., Szaszi, B., Sarafoglou, A., Kekecs, Z., Kucharský, Š., Benjamin, D., . . . Wagenmakers, E.-J. (2020). A consensus-based transparency checklist. Nature Human Behaviour, 4(1), 4-6. doi:10.1038/s41562-019-0772-6
A priori power analysis calculates N for a null hypothesis test with specified sensitivity and specificity
How can these recommendations be reconciled?
What is the best way of choosing a sample size without any reliance on null-hypothesis tests?
Particularly if investigator does not have access to Bayes Software, or is a frequentist at heart
Many thanks for any help
best
Diana
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