scholarly journals Adopting a Meta-Generative Way of Thinking in the Field of Education via the Use of Bayesian Methods: A Multimethod Approach in a Post-Truth and COVID-19 Era1

2020 ◽  
pp. 4-19 ◽  
Author(s):  
Prathiba Natesan Batley ◽  
Peter Boedeker ◽  
Anthony J. Onwuegbuzie
Keyword(s):  
Bayesian Methods ◽  
Null Hypothesis ◽  
Quantitative Research ◽  
Meta Analysis ◽  
Research Synthesis ◽  
Research Data ◽  
Policy Implications ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Multimethod Approach

In this editorial, we introduce the multimethod concept of thinking meta-generatively, which we define as directly integrating findings from the extant literature during the data collection, analysis, and interpretation phases of primary studies. We demonstrate that meta-generative thinking goes further than do other research synthesis techniques (e.g., meta-analysis) because it involves meta-synthesis not only across studies but also within studies—thereby representing a multimethod approach. We describe how meta-generative thinking can be maximized/optimized with respect to quantitative research data/findings via the use of Bayesian methodology that has been shown to be superior to the inherently flawed null hypothesis significance testing. We contend that Bayesian meta-generative thinking is essential, given the potential for divisiveness and far-reaching sociopolitical, educational, and health policy implications of findings that lack generativity in a post-truth and COVID-19 era.

Statistical significance testing, hypothetico-deductive method, and theory evaluation

2000 ◽  
Vol 23 (2) ◽  
pp. 292-293 ◽  
Author(s):  
Brian D. Haig
Keyword(s):  
Null Hypothesis ◽  
Meta Analysis ◽  
Statistical Significance ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Statistical Significance Testing ◽  
Theory Evaluation ◽  
Limited Role ◽  
Deductive Method

Chow's endorsement of a limited role for null hypothesis significance testing is a needed corrective of research malpractice, but his decision to place this procedure in a hypothetico-deductive framework of Popperian cast is unwise. Various failures of this version of the hypothetico-deductive method have negative implications for Chow's treatment of significance testing, meta-analysis, and theory evaluation.

scholarly journals Ο έλεγχος μηδενικών υποθέσεων: διαδικασία, παρανοήσεις και μερικές προτάσεις για καλύτερες πρακτικές

2020 ◽  
Vol 18 (2) ◽  
pp. 224
Author(s):  
Πέτρος Ρούσσος
Keyword(s):  
Confidence Intervals ◽  
Null Hypothesis ◽  
Research Data ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Research Results ◽  
Error Bars ◽  
Reform Debate

The rationale of Null Hypothesis Significance Testing (NHST) is described, and the consequences of its hybridism are discussed. The paper presents examples published in “PSYCHOLOGY: The Journal of the HPS” refer to NHST and interpret its outcomes. We examined the 445 articles published between 1992 and 2010. We noted misuses of NHST and searched for any use of confidence intervals or error bars or use of these to support interpretation. Part of the paper focuses on the statistical-reform debate and provides detailed guidance about good statistical practices in the analysis of research data and the interpretation of findings. The proposed guide does not fall into the trap of mandating the use of particular procedures; it rather aims to support readers’ understanding of research results.

The logic of null hypothesis testing

1998 ◽  
Vol 21 (2) ◽  
pp. 197-198 ◽  
Author(s):  
Edward Erwin
Keyword(s):  
Hypothesis Testing ◽  
Null Hypothesis ◽  
Power Analysis ◽  
Meta Analysis ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Null Hypothesis Testing

In this commentary, I agree with Chow's treatment of null hypothesis significance testing as a noninferential procedure. However, I dispute his reconstruction of the logic of theory corroboration. I also challenge recent criticisms of NHSTP based on power analysis and meta-analysis.

Bayesian Statistics in Archaeology

2018 ◽  
Vol 47 (1) ◽  
pp. 435-453 ◽  
Author(s):  
Erik Otárola-Castillo ◽  
Melissa G. Torquato
Keyword(s):  
Bayesian Inference ◽  
Bayesian Statistics ◽  
Bayesian Methods ◽  
Null Hypothesis ◽  
Significance Testing ◽  
Archaeological Research ◽  
Historical Overview ◽  
Spatial Analyses ◽  
Null Hypothesis Significance Testing ◽  
Statistical Framework

Null hypothesis significance testing (NHST) is the most common statistical framework used by scientists, including archaeologists. Owing to increasing dissatisfaction, however, Bayesian inference has become an alternative to these methods. In this article, we review the application of Bayesian statistics to archaeology. We begin with a simple example to demonstrate the differences in applying NHST and Bayesian inference to an archaeological problem. Next, we formally define NHST and Bayesian inference, provide a brief historical overview of their development, and discuss the advantages and limitations of each method. A review of Bayesian inference and archaeology follows, highlighting the applications of Bayesian methods to chronological, bioarchaeological, zooarchaeological, ceramic, lithic, and spatial analyses. We close by considering the future applications of Bayesian statistics to archaeological research.

scholarly journals When the Numbers Do Not Add Up: The Practical Limits of Stochastologicals for Soft Psychology

2021 ◽  
pp. 174569162097055
Author(s):  
Nick J. Broers
Keyword(s):  
High Risk ◽  
Null Hypothesis ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Psychological Science ◽  
Body Of Knowledge ◽  
Psychological Theories ◽  
Time Continuum

One particular weakness of psychology that was left implicit by Meehl is the fact that psychological theories tend to be verbal theories, permitting at best ordinal predictions. Such predictions do not enable the high-risk tests that would strengthen our belief in the verisimilitude of theories but instead lead to the practice of null-hypothesis significance testing, a practice Meehl believed to be a major reason for the slow theoretical progress of soft psychology. The rising popularity of meta-analysis has led some to argue that we should move away from significance testing and focus on the size and stability of effects instead. Proponents of this reform assume that a greater emphasis on quantity can help psychology to develop a cumulative body of knowledge. The crucial question in this endeavor is whether the resulting numbers really have theoretical meaning. Psychological science lacks an undisputed, preexisting domain of observations analogous to the observations in the space-time continuum in physics. It is argued that, for this reason, effect sizes do not really exist independently of the adopted research design that led to their manifestation. Consequently, they can have no bearing on the verisimilitude of a theory.

scholarly journals Bayesian evaluation of effect size after replicating an original study

2017 ◽  
Author(s):  
Robbie Cornelis Maria van Aert ◽  
Marcel A. L. M. van Assen
Keyword(s):  
Effect Size ◽  
Web Application ◽  
Null Hypothesis ◽  
Meta Analysis ◽  
Original Study ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
True Effect Size ◽  
Psychology And Economics ◽  
Zero Effect

The vast majority of published results in the literature is statistically significant, which raises concerns about their reliability. The Reproducibility Project Psychology (RPP) and Experimental Economics Replication Project (EE-RP) both replicated a large number of published studies in psychology and economics. The original study and replication were statistically significant in 36.1% in RPP and 68.8% in EE-RP suggesting many null effects among the replicated studies. However, evidence in favor of the null hypothesis cannot be examined with null hypothesis significance testing. We developed a Bayesian meta-analysis method called snapshot hybrid that is easy to use and understand and quantifies the amount of evidence in favor of a zero, small, medium and large effect. The method computes posterior model probabilities for a zero, small, medium, and large effect and adjusts for publication bias by taking into account that the original study is statistically significant. We first analytically approximate the methods performance, and demonstrate the necessity to control for the original study’s significance to enable the accumulation of evidence for a true zero effect. Then we applied the method to the data of RPP and EE-RP, showing that the underlying effect sizes of the included studies in EE-RP are generally larger than in RPP, but that the sample sizes of especially the included studies in RPP are often too small to draw definite conclusions about the true effect size. We also illustrate how snapshot hybrid can be used to determine the required sample size of the replication akin to power analysis in null hypothesis significance testing and present an easy to use web application (https://rvanaert.shinyapps.io/snapshot/) and R code for applying the method.

scholarly journals When the numbers do not add up: the practical limits of stochastologicals for soft psychology

2021 ◽  
Author(s):  
Nick J. Broers
Keyword(s):  
High Risk ◽  
Null Hypothesis ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Psychological Science ◽  
Body Of Knowledge ◽  
Psychological Theories ◽  
Time Continuum

One particular weakness of psychology that was left implicit by Meehl (1978) is the fact that psychological theories tend to be verbal theories, permitting at best ordinal predictions. Such predictions do not enable the high risk tests that would strengthen our belief in the verisimilitude of theories but instead lead to the practice of null hypothesis significance testing, a practice Meehl believed to be a major reason for the slow theoretical progress of soft psychology. The rising popularity of meta-analysis has led some to argue that we should move away from significance testing and focus on the size and stability of effects instead. Proponents of this reform assume that a greater emphasis on quantity can help psychology to develop a cumulative body of knowledge. The crucial question in this endeavor is whether the resulting numbers really have theoretical meaning. Psychological science lacks an undisputed, pre-existing domain of observations analogous to the observations in the space-time continuum in physics. It is argued that for this reason effect sizes do not really exist independently of the adopted research design that led to their manifestation. Consequently, they can have no bearing on the verisimilitude of a theory.

scholarly journals A Frequentist Alternative to Significance Testing, p-Values, and Confidence Intervals

Econometrics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 26 ◽  
Author(s):  
David Trafimow
Keyword(s):  
Present Article ◽  
Confidence Intervals ◽  
Null Hypothesis ◽  
A Priori ◽  
Significance Testing ◽  
Population Parameters ◽  
Null Hypothesis Significance Testing ◽  
P Values ◽  
Statistical Procedures ◽  
Major Section

There has been much debate about null hypothesis significance testing, p-values without null hypothesis significance testing, and confidence intervals. The first major section of the present article addresses some of the main reasons these procedures are problematic. The conclusion is that none of them are satisfactory. However, there is a new procedure, termed the a priori procedure (APP), that validly aids researchers in obtaining sample statistics that have acceptable probabilities of being close to their corresponding population parameters. The second major section provides a description and review of APP advances. Not only does the APP avoid the problems that plague other inferential statistical procedures, but it is easy to perform too. Although the APP can be performed in conjunction with other procedures, the present recommendation is that it be used alone.

The Numbers Will Love You Back in Return—I Promise

2016 ◽  
Vol 11 (4) ◽  
pp. 551-554 ◽  
Author(s):  
Martin Buchheit
Keyword(s):  
Sample Size ◽  
Null Hypothesis ◽  
Clinical Medicine ◽  
Statistical Significance ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Sport Science ◽  
Size Dependent ◽  
Research Questions ◽  
Per Se

The first sport-science-oriented and comprehensive paper on magnitude-based inferences (MBI) was published 10 y ago in the first issue of this journal. While debate continues, MBI is today well established in sport science and in other fields, particularly clinical medicine, where practical/clinical significance often takes priority over statistical significance. In this commentary, some reasons why both academics and sport scientists should abandon null-hypothesis significance testing and embrace MBI are reviewed. Apparent limitations and future areas of research are also discussed. The following arguments are presented: P values and, in turn, study conclusions are sample-size dependent, irrespective of the size of the effect; significance does not inform on magnitude of effects, yet magnitude is what matters the most; MBI allows authors to be honest with their sample size and better acknowledge trivial effects; the examination of magnitudes per se helps provide better research questions; MBI can be applied to assess changes in individuals; MBI improves data visualization; and MBI is supported by spreadsheets freely available on the Internet. Finally, recommendations to define the smallest important effect and improve the presentation of standardized effects are presented.

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