@article{Saint-Mont_2018, title={Where Fisher, Neyman and Pearson went astray: On the logic (plus some history and philosophy) of Statistical Tests}, volume={5}, url={https://journals.scholarpublishing.org/index.php/ASSRJ/article/view/4867}, DOI={10.14738/assrj.58.4867}, abstractNote={<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Every scientific endeavour consists of (at least) two components: A hypothesis on the one hand and data on the other. There is always a more or less abstract level - some theory, a set of concepts, certain relations of ideas - and a concrete level, i.e., empirical evidence, experiments or some observations which constitute matters of fact. </span>The focus of this contribution is on elementary models connecting both levels that have been very popular in the social sciences - statistical tests. Going from simple to complex we will examine four paradigms of statistical testing (Fisher, Likelihood, Bayes, Neyman &amp; Pearson) and an elegant contemporary treatment. In a nutshell, testing is an easy problem that has a straightforward mathematical solution. However, it is rather surprising that the statistical mainstream has pursued a different line of argument. The application of the latter theory in psychology and other fields has brought some progress but has also impaired scientific thinking.</p></div></div></div>}, number={8}, journal={Advances in Social Sciences Research Journal}, author={Saint-Mont, Uwe}, year={2018}, month={Aug.} }