Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item P-values as percentiles. Commentary on: "Null hypothesis significance tests. A mix-up of two different theories: the basis for widespread confusion and numerous misinterpretations".(FRONTIERS RESEARCH FOUNDATION, 2015) Perezgonzalez JDItem The fallacy of placing confidence in confidence intervals – A commentary(Open Science Framework (OSF), 2/05/2017) Perezgonzalez JD‘The fallacy of placing confidence in confidence intervals’ (Morey et al., 2016, Psychonomic Bulletin & Review, doi: 10.3758/s13423-015-0947-8) delved into a much needed technical and philosophical dissertation regarding the differences between typical (mis)interpretations of frequentist confidence intervals and the typical correct interpretation of Bayesian credible intervals. My contribution here partly strengthens the authors’ argument, partly closes some gaps they left open, and concludes with a note of attention to the possibility that there may be distinctions without real practical differences in the ultimate use of estimation by intervals, namely when assuming a common ground of uninformative priors and intervals as ranges of values instead of as posterior distributions per se.Item Commentary: Psychological Science’s Aversion to the Null(Frontiers Media SA, 9/06/2020) Perezgonzalez JD; Frias-Navarro D; Pascual-Llobell J; Dettweiler, U; Hanfstingl, B; Schroter, HHeene and Ferguson (2017) contributed important epistemological, ethical and didactical ideas to the debate on null hypothesis significance testing, chief among them ideas about falsificationism, statistical power, dubious statistical practices, and Publication bias. Important as those contributions are, the authors do not fully resolve four confusions which we would like to clarify.Item Commentary: The Need for Bayesian Hypothesis Testing in Psychological Science.(2017) Perezgonzalez JDItem Fisher, Neyman-Pearson or NHST? A tutorial for teaching data testing.(FRONTIERS RESEARCH FOUNDATION, 2015) Perezgonzalez JDDespite frequent calls for the overhaul of null hypothesis significance testing (NHST), this controversial procedure remains ubiquitous in behavioral, social and biomedical teaching and research. Little change seems possible once the procedure becomes well ingrained in the minds and current practice of researchers; thus, the optimal opportunity for such change is at the time the procedure is taught, be this at undergraduate or at postgraduate levels. This paper presents a tutorial for the teaching of data testing procedures, often referred to as hypothesis testing theories. The first procedure introduced is Fisher's approach to data testing-tests of significance; the second is Neyman-Pearson's approach-tests of acceptance; the final procedure is the incongruent combination of the previous two theories into the current approach-NSHT. For those researchers sticking with the latter, two compromise solutions on how to improve NHST conclude the tutorial.Item Open letter to The Independent - Pilots 'very likely' to misjudge flying conditions due to irrational decisions, revisited(Figshare, 22/12/2016) Perezgonzalez JDStaufenberg’s news article (2016) comments on research reported by Walmsley and Gilbey (2016). An interview with the corresponding author also yielded extra information, especially the verbalization that practically all pilots fell prey to cognitive biases and the hint that pilots were making irrational decisions.In reality, Walmsley and Gilbey’s own results do not support much of the conclusions posed. I have further expanded on information which is specific to Staufenberg’s news article, especially information about minima meteorological conditions for visual flight rules (VFR) flying in the UK, as well as a breakdown of the percentage of pilots in Walmsley and Gilbey’s study which contradicts the information provided.Item Confidence intervals and tests are two sides of the same research question.(FRONTIERS RESEARCH FOUNDATION, 2015) Perezgonzalez JDItem Failings in COPE's guidelines to editors, and recommendations for improvement.(Figshare, 23/11/2016) Perezgonzalez JDLetter highlighting failings in COPE's Guidelines to editors and proposing recommendations for improvement. The main recommendation is to create appropriate guidelines for dealing with fully disclosed (potential) conflicts of interest. COPE sought the topic as relevant and included a session on the topic as part of COPE's Forum (Feb 3, 2017; http://publicationethics.org/forum-discussion-topic-comments-please-7).Item Statistical Sensitiveness for the Behavioral Sciences(Open Science Framework (OSF), 14/02/2017) Perezgonzalez JDResearch often necessitates of samples, yet obtaining large enough samples is not always possible. When it is, the researcher may use one of two methods for deciding upon the required sample size: rules-of-thumb, quick yet uncertain, and estimations for power, mathematically precise yet with the potential to overestimate or underestimate sample sizes when effect sizes are unknown. Misestimated sample sizes have negative repercussions in the form of increased costs, abandoned projects or abandoned publication of non-significant results. Here I describe a procedure for estimating sample sizes adequate for the testing approach which is most common in the behavioural, social, and biomedical sciences, that of Fisher’s tests of significance. The procedure focuses on a desired minimum effect size for the research at hand and finds the minimum sample size required for capturing such effect size as a statistically significant result. In a similar fashion than power analyses, sensitiveness analyses can also be extended to finding the minimum effect for a given sample size a priori as well as to calculating sensitiveness a posteriori. The article provides a full tutorial for carrying out a sensitiveness analysis, as well as empirical support via simulation.Item Retract p < 0.005 and propose using JASP, instead(F1000Research, 12/12/2017) Perezgonzalez JD; Frías-Navarro MDSeeking to address the lack of research reproducibility in science, including psychology and the life sciences, a pragmatic solution has been raised recently: to use a stricter p < 0.005 standard for statistical significance when claiming evidence of new discoveries. Notwithstanding its potential impact, the proposal has motivated a large mass of authors to dispute it from different philosophical and methodological angles. This article reflects on the original argument and the consequent counterarguments, and concludes with a simpler and better-suited alternative that the authors of the proposal knew about and, perhaps, should have made from their Jeffresian perspective: to use a Bayes factors analysis in parallel (e.g., via JASP) in order to learn more about frequentist error statistics and about Bayesian prior and posterior beliefs without having to mix inconsistent research philosophies.
