Browsing by Author "Perezgonzalez JD"

Now showing 1 - 20 of 22
  • Thumbnail Image
    Item
    Book Review: Another Science Is Possible
    (Frontiers Media S.A., 2018-04-04) Perezgonzalez JD; Frias-Navarro D; Pascual-Llobell J; Dettweiler U
  • Thumbnail Image
    Item
    Book Review: Skin in the Game
    (Frontiers Media S.A., 2018-09-04) Perezgonzalez JD; Soylu, MY
  • Thumbnail Image
    Item
    Book Review: Surgery, the Ultimate Placebo
    (Frontiers Media S.A., 2018-05-25) Perezgonzalez JD; Nikolaou VS
  • Thumbnail Image
    Item
    Commentary: Continuously cumulating meta-analysis and replicability.
    (FRONTIERS RESEARCH FOUNDATION, 2015) Perezgonzalez JD
  • Thumbnail Image
    Item
    Commentary: How Bayes factors change scientific practice
    (Frontiers Media, 30/09/2016) Perezgonzalez JD
    Dienes's (2016) article is one of the contributions to the special issue “Bayes factors for testing hypotheses in psychological research…” being published by the Journal of Mathematical Psychology. One concern I have with Dienes's article is its “one-size-fits-all” philosophy. A second concern is the reification of Bayes factors as the solution to the credibility crisis. I find it naive that a single approach is still proposed as the one and only tool for testing data. I also find it naive to assume that Bayes factors, with no clear replicability mechanism attached to them, are the ones to resolve the credibility crisis in psychology.
  • Thumbnail Image
    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, H
    Heene 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.
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
    Confidence intervals and tests are two sides of the same research question.
    (FRONTIERS RESEARCH FOUNDATION, 2015) Perezgonzalez JD
  • Thumbnail Image
    Item
    Failings in COPE's guidelines to editors, and recommendations for improvement.
    (Figshare, 23/11/2016) Perezgonzalez JD
    Letter 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).
  • Thumbnail Image
    Item
    Fisher, Neyman-Pearson or NHST? A tutorial for teaching data testing
    (Frontiers Media SA, 2016-11-11) Perezgonzalez JD; Roberts, LD
  • Thumbnail Image
    Item
    Fisher, Neyman-Pearson or NHST? A tutorial for teaching data testing.
    (FRONTIERS RESEARCH FOUNDATION, 2015) Perezgonzalez JD
    Despite 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.
  • Thumbnail Image
    Item
    Manipulating the alpha level cannot cure significance testing – comments on "Redefine statistical significance"
    (PeerJ Preprints, 2017-11-14) Trafimov D; Amrhein V; Areshenkoff CN; Barrera - Causil C; Beh EJ; Bilgiç Y; Bono R; Bradley MT; Briggs WM; Cepeda - Freyre HA; Chaigneau SE; Ciocca DR; Correa JC; Cousineau D; de Boer MR; Dhar SS; Dolgov I; Gómez - Benito J; Grendar M; Grice J; Guerrero - Gimenez ME; Gutiérrez A; Huedo - Medina TB; Jaffe K; Janyan A; Karimnezhad A; Korner - Nievergelt F; Kosugi K; Lachmair M; Ledesma R; Limongi R; Liuzza MT; Lombardo R; Marks M; Meinlschmidt G; Nalborczyk L; Nguyen HT; Ospina R; Perezgonzalez JD; Pfister R; Rahona JJ; Rodríguez - Medina DA; Romão X; Ruiz - Fernández S; Suarez I; Tegethoff M; Tejo M; van de Schoot R; Vankov I; Velasco - Forero S; Wang T; Yamada Y; Zoppino FCM; Marmolejo - Ramos F
    We argue that depending on p-values to reject null hypotheses, including a recent call for changing the canonical alpha level for statistical significance from .05 to .005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable criterion levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and determining sample sizes much more directly than significance testing does; but none of the statistical tools should replace significance testing as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, or implications for applications. To boil all this down to a binary decision based on a p-value threshold of .05, .01, .005, or anything else, is not acceptable.
  • Thumbnail Image
    Item
    Open letter to The Independent - Pilots 'very likely' to misjudge flying conditions due to irrational decisions, revisited
    (Figshare, 22/12/2016) Perezgonzalez JD
    Staufenberg’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.
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
    Retract 0.005 and propose using JASP, instead
    (F1000 Research Ltd, 2017-11-29) Perezgonzalez JD; Frias-Navarro MD
    Seeking 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.
  • Thumbnail Image
    Item
    Retract p < 0.005 and propose using JASP, instead
    (F1000Research, 12/12/2017) Perezgonzalez JD; Frías-Navarro MD
    Seeking 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.
  • Thumbnail Image
    Item
    Sorry to say, but pilots’ decisions were not irrational
    (British Psychological Society Digest Research, 16/12/2016) Perezgonzalez JD
    Fradera’s Digest (2016) makes for interesting reading both for aviators and cognitive psychologists alike. Fradera reports on a research article by Walmsley and Gilbey (2016) and the Digest seems pretty accurate to the contents commented upon (in a way, thus, whatever praises or criticisms are raised apply equally to the latter article). The Digest is interesting because what it says is quite relevant in principle but rather misleading in practice. That is, the actual results reported by Walmsley and Gilbey, do not seem to support the portrayal of pilots as biased and irrational, a portrayal which originates in the interpretation of those results based on a flawed statistical technique—null hypothesis significance testing, or NHST. In a nutshell, Fradera opted to summarize the interpretation of (some) outputs made by Walmsley and Gilbey instead of re-interpreting those outputs anew within the context of the methodology and the results described in the original article, as I shall argue.
  • Thumbnail Image
    Item
    Statistical Inference as Severe Testing. How to Get Beyond the Statistics Wars
    (Frontiers Media S.A., 2019-04-05) Perezgonzalez JD; Pascual-Soler M; Pascual-Llobell J; Frias-Navarro D; Coxe S
  • Thumbnail Image
    Item
    Statistical Sensitiveness for the Behavioral Sciences
    (Open Science Framework (OSF), 14/02/2017) Perezgonzalez JD
    Research 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.
  • Thumbnail Image
    Item
    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.