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Item Statistics anxiety in university students in assessment situations(Editorial Universitat Politècnica de València, 2018) Frias-Navarro D; Monterde-i-Bort H; Navarro-Gonzalez N; Molina-Palomero O; Pascual-Soler M; Perezgonzalez J; Longobardi C; Domenech J; Merello P; de la Poza E; Blazquez DMany students have feelings of state anxiety when taking exams, and these feelings probably affect their performance. Statistics courses have been identified as producing the most anxiety. The purpose of our study is to measure statistics anxiety throughout an academic course (pre-test and three assessments) in order to observe its change and analyze the relationship between statistics anxiety and academic achievement. The sample is composed of 30 Psychology students taking a course in research designs and statistics (26.7% men and 73.3% women) with a mean age of 20.31 years (SD = 3.76). The results show that the students begin with a high level of statistics anxiety that gradually declines as the course progresses and they study the course materials. Moreover, the final achievement in the subject maintains an inverse relationship with the level of statistics anxiety. The recommendation is to present the detailed contents of the teaching guide on the first day of the course in order to reduce students’ anxiety and uncertainty when beginning a statistics course. Financial support: Project UV-INV-AE17-698616. University of Valencia. Spain.Item Frequentist-Bayesian analyses in parallel using JASP - A tutorial(PsyArXiv, 2022-07-21) Perezgonzalez JA tutorial to demonstrate the use of parallel Frequentist-Bayesian analyses using JASP, and the plausible inferences one may be able to make from such combined analysis.Item Rapid Antigen Tests (RATs) and COVID-19 prevalence(OSF Preprins, 2022-05-04) Perezgonzalez JThe article contains a Bayesian analysis to model expected rate of positive and negative COVID-19 cases, based on Rapid Antigen Test performance and COVID-19 prevalence in New Zealand. The results suggest that the majority of approved tests were excellent in identifying negative cases but might turn out too many false positives. Recommendations for a protocol for RAT-based testing concludes the article.Item Where are our false positives?(OSF Preprints, 2022-05-03) Perezgonzalez JIn our current regime of COVID-19 testing, a question seems not to be asked: Are we inferring the best we can from our results? Or, put differently, are we testing with severity? This study thus explore the proportion of expected positives and negative cases, with an especial focus on estimating false positives in isolation and estimating false (or unknown) negatives in the remaining population. Both seems to have been chiefly ignored by Government health policy.Item Severity Testing: A Primer(2020-04-01) Perezgonzalez JThis is a primer on Mayo's severity testing technology, briefly explaining step by step the implications of severity in the context of rejection and retention of point nil hypotheses, of (conceptually broader) null hypotheses, and of confidence intervals. I finish proposing the use of a confidence interval heuristic for assessing Mayo's severity straightforwardly. In the present article we shall not concern ourselves with wars, whether statistical or philosophical. Instead, we shall work on a philosophical concept being put forth by Mayo in the last two decades (e.g., Mayo, 1983, 1991, 1996; Mayo & Spanos, 2006, 2010), more recently so with her book Statistical Inference as Severe Testing (Mayo, 2018). Severity testing stands for a procedure a researcher can avail of to falsify (frequentist) hypotheses; yet it may spill beyond that bin to become a broader philosophical approach that can serve to also falsify (Bayesian) models (Gelman & Shalizi, 2013), even entire theories. The article I present here is going to be a primer on Mayo’s severity concept in the frequentist realm. However, I sympathise with Gelman and reckon the unstated goal is to advance such primer as a step towards using severity in line with Gelman’s ideas and further, including Taleb’s own use of falsification as a tool to get more acquainted with what our models and theories prevents us from learning, such as about extreme events and Black Swans (e.g., Taleb, 2005, 2010).Item Book Review: Bayesian Statistics the Fun Way: Understanding Statistics and Probability With Star Wars, Lego, and Rubber Ducks(Frontiers Media, 15/01/2020) Perezgonzalez JBayesian Statistics the Fun Way is an engaging introduction to Bayesian inference by Kurt (2019). His main goal of producing “a book on Bayesian statistics that really anyone could pick up and use to gain real intuitions for how to think statistically and solve real problems using statistics” (Carrone, 2019) is certainly achieved. Indeed, the book introduces Bayesian methods in a clear and concise manner, without assuming prior statistical knowledge and, for the most part, eschewing formulations. It explores Bayesian inference in a very intuitive way and with engaging examples—from UFOs to conspiracy theorists, via Lego, crime scenes, Start Wars, email click baits, and funfair rubber ducks—and constrains itself well enough for readers to start applying Bayesian inference from the word go.Item Another Science Is Possible(Frontiers Media, 8/06/2020) Perezgonzalez J; Frias-Navarro D; Pascual-Llobell J; Dettweiler, U; Hanfstingl, B; Schroter, HThe philosopher of science Isabelle Stengers provides some food for thought regarding both the way we are doing science and the need for an alternative approach likened to the slow movement in other spheres of life.Item Book Review: Bayesian Statistics for Beginners. A Step-by-Step Approach(Frontiers Media, 19/05/2020) Perezgonzalez JBayesian Statistics for Beginners. A Step-by-Step Approach (Donovan and Mickey, 2019) is, perhaps, the “truest-to-title” book I have read on Bayesian inference and statistics, insofar (a) it is written for novices to probability, inference, the scientific method, and Bayesian methodology, (b) it introduces those four topics step-by-step, repeats them as needed, and emphasizes them throughout the entire book, and (c), despite the authors claiming that “this is not meant to be a course on statistics”(p. 269), the book delves into enough Bayesian statistics to last a lifetime. The most important contribution, however, is that this is a book purposely devoted to highlighting the role of Bayesian methodology and inference in the conduct of science.
