Bayesian frequentists: Examining the paradox between what researchers can conclude versus what they want to conclude from statistical results

A majority of statistically educated scientists draw incorrect conclusions based on, arguably, the most commonly used statistical technique: null hypothesis significance testing (NHST). Frequentist techniques are often claimed to be incorrectly interpreted as Bayesian outcomes, which suggests that a Bayesian framework may fit better to inferences researchers frequently want to make (Briggs, 2012). The current study set out to test this proposition. Firstly, we investigated whether there is a discrepancy between what researchers think they can conclude and what they want to be able to conclude from NHST. Secondly, we investigated to what extent researchers want to incorporate prior study results and subjective beliefs in their statistical inference. Results show the expected discrepancy between what researchers think they can conclude from NHST and what they want to be able to conclude. Furthermore, researchers were interested in incorporating prior study results, but not subjective beliefs, into their statistical inference.