scholarly journals Against statistical significance testing in corpus linguistics

2019 ◽  
Vol 15 (2) ◽  
pp. 321-346 ◽  
Author(s):  
Alexander Koplenig
Keyword(s):  
Corpus Linguistics ◽  
Null Hypothesis ◽  
Statistical Significance ◽  
Linguistic Theory ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Statistical Significance Testing ◽  
Naturally Occurring ◽  
Language Data

Abstract In the first volume of Corpus Linguistics and Linguistic Theory, Gries (2005. Null-hypothesis significance testing of word frequencies: A follow-up on Kilgarriff. Corpus Linguistics and Linguistic Theory 1(2). doi:10.1515/cllt.2005.1.2.277. http://www.degruyter.com/view/j/cllt.2005.1.issue-2/cllt.2005.1.2.277/cllt.2005.1.2.277.xml: 285) asked whether corpus linguists should abandon null-hypothesis significance testing. In this paper, I want to revive this discussion by defending the argument that the assumptions that allow inferences about a given population – in this case about the studied languages – based on results observed in a sample – in this case a collection of naturally occurring language data – are not fulfilled. As a consequence, corpus linguists should indeed abandon null-hypothesis significance testing.

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.

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.

scholarly journals On the Potential Mismatch between the Function of the Bayes Factor and Researchers’ Expectations

2021 ◽  
Author(s):  
Tsz Keung Wong ◽  
Henk Kiers ◽  
Jorge Tendeiro
Keyword(s):  
Null Hypothesis ◽  
Bayes Factor ◽  
Survey Study ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Posterior Odds ◽  
Statistical Tool ◽  
Reporting Practices ◽  
Insight Into

The aim of this study is to investigate whether there is a potential mismatch between the usability of a statistical tool and psychology researchers’ expectation of it. Bayesian statistics is often promoted as an ideal substitute for frequentists statistics since it coincides better with researchers’ expectations and needs. A particular incidence of this is the proposal of replacing Null Hypothesis Significance Testing (NHST) by Null Hypothesis Bayesian Testing (NHBT) using the Bayes factor. In this paper, it is studied to what extent the usability and expectations of NHBT match well. First, a study of the reporting practices in 73 psychological publications was carried out. It was found that eight Questionable Reporting and Interpreting Practices (QRIPs) occur more than once among the practitioners when doing NHBT. Specifically, our analysis provides insight into possible mismatches and their occurrence frequencies. A follow-up survey study has been conducted to assess such mismatches. The sample (N = 108) consisted of psychology researchers, experts in methodology (and/or statistics), and applied researchers in fields other than psychology. The data show that discrepancies exist among the participants. Interpreting the Bayes Factor as posterior odds and not acknowledging the notion of relative evidence in the Bayes Factor are arguably the most concerning ones. The results of the paper suggest that a shift of statistical paradigm cannot solve the problem of misinterpretation altogether if the users are not well acquainted with the tools.

scholarly journals Failing Grade: 89% of Introduction-to-Psychology Textbooks That Define or Explain Statistical Significance Do So Incorrectly

2019 ◽  
Vol 2 (3) ◽  
pp. 233-239 ◽  
Author(s):  
Scott A. Cassidy ◽  
Ralitza Dimova ◽  
Benjamin Giguère ◽  
Jeffrey R. Spence ◽  
David J. Stanley
Keyword(s):  
United States ◽  
Null Hypothesis ◽  
Classroom Instruction ◽  
Statistical Significance ◽  
Psychology Students ◽  
The United States ◽  
Introductory Psychology ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Do So

Null-hypothesis significance testing (NHST) is commonly used in psychology; however, it is widely acknowledged that NHST is not well understood by either psychology professors or psychology students. In the current study, we investigated whether introduction-to-psychology textbooks accurately define and explain statistical significance. We examined 30 introductory-psychology textbooks, including the best-selling books from the United States and Canada, and found that 89% incorrectly defined or explained statistical significance. Incorrect definitions and explanations were most often consistent with the odds-against-chance fallacy. These results suggest that it is common for introduction-to-psychology students to be taught incorrect interpretations of statistical significance. We hope that our results will create awareness among authors of introductory-psychology books and provide the impetus for corrective action. To help with classroom instruction, we provide slides that correctly describe NHST and may be useful for introductory-psychology instructors.

Tests of Statistical Significance

2018 ◽  
Author(s):  
Brian D. Haig
Keyword(s):  
Null Hypothesis ◽  
Statistical Significance ◽  
Lessons Learned ◽  
Significance Testing ◽  
Behavioral Sciences ◽  
Null Hypothesis Significance Testing ◽  
Tests Of Significance ◽  
Philosophy Of Statistics ◽  
Critical Literature

Chapter 3 provides a brief overview of null hypothesis significance testing and points out its primary defects. It then outlines the neo-Fisherian account of tests of statistical significance, along with a second option contained in the philosophy of statistics known as the error-statistical philosophy, both of which are defensible. Tests of statistical significance are the most widely used means for evaluating hypotheses and theories in psychology. A massive critical literature has developed in psychology, and the behavioral sciences more generally, regarding the worth of these tests. The chapter provides a list of important lessons learned from the ongoing debates about tests of significance.

Costs and benefits of statistical significance tests

1998 ◽  
Vol 21 (2) ◽  
pp. 218-219
Author(s):  
Michael G. Shafto
Keyword(s):  
Null Hypothesis ◽  
Statistical Significance ◽  
Testing Procedure ◽  
Significance Testing ◽  
Costs And Benefits ◽  
Significance Tests ◽  
Null Hypothesis Significance Testing ◽  
Social Scientists ◽  
Conventional Tests

Chow's book provides a thorough analysis of the confusing array of issues surrounding conventional tests of statistical significance. This book should be required reading for behavioral and social scientists. Chow concludes that the null-hypothesis significance-testing procedure (NHSTP) plays a limited, but necessary, role in the experimental sciences. Another possibility is that – owing in part to its metaphorical underpinnings and convoluted logic – the NHSTP is declining in importance in those few sciences in which it ever played a role.

Lady Justice Versus Cult of Statistical Significance

2015 ◽  
pp. 351-364
Author(s):  
Stephen T. Ziliak ◽  
Deirdre McCloskey
Keyword(s):  
Loss Function ◽  
Null Hypothesis ◽  
Statistical Significance ◽  
Ethical Problem ◽  
Supreme Court Decision ◽  
Court Decision ◽  
Significance Testing ◽  
Statistical Significance Testing ◽  
Economic Significance ◽  
Random Variance

Economics and other sciences use null hypothesis statistical significance testing without a loss function and avoid asking “how big is a big loss or gain?.” Statistical significance is not equivalent to economic significance; the mistake is evident when one reflects that the estimated payoff from a lottery is not the same as the odds of winning that lottery. Yet a widespread failure to make the distinction between an estimate of human consequence and an estimate of its probability—between the meaning of an estimated average and the random variance around it—is killing people in medicine and impoverishing people in economics. The ethical problem created by a test of statistical significance is made worse by the method’s blatant illogic at the foundational level, a fact unacknowledged by most of those depending on it. Several changes to the literature and a recent Supreme Court decision could help.

scholarly journals Do psychology students interpret null hypothesis significance testing critically?

2017 ◽  
Author(s):  
Ivan Flis
Keyword(s):  
Hypothesis Testing ◽  
Null Hypothesis ◽  
Statistical Significance ◽  
Psychology Students ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Graduate Studies ◽  
Null Hypothesis Testing ◽  
Level Of Understanding ◽  
Grade Average

The goal of the study was to descriptively analyze the understanding of null hypothesis significance testing among Croatian psychology students considering how it is usually understood in textbooks, which is subject to Bayesian and interpretative criticism. Also, the thesis represents a short overview of the discussions on the meaning of significance testing and how it is taught to students. There were 350 participants from undergraduate and graduate programs at five faculties in Croatia (Zagreb – Centre for Croatian Studies and Faculty of Humanities and Social Sciences, Rijeka, Zadar, Osijek). Another goal was to ascertain if the understanding of null hypothesis testing among psychology students can be predicted by their grades, attitudes and interests. The level of understanding of null hypothesis testing was measured by the Test of statistical significance misinterpretations (NHST test) (Oakes, 1986; Haller and Krauss, 2002). The attitudes toward null hypothesis significance testing were measured by a questionnaire that was constructed for this study. The grades were operationalized as the grade average of courses taken during undergraduate studies, and as a separate grade average of methodological courses taken during undergraduate and graduate studies. The students have shown limited understanding of null hypothesis testing – the percentage of correct answers in the NHST test was not higher than 56% for any of the six items. Croatian students have also shown less understanding on each item when compared to the German students in Haller and Krauss’s (2002) study. None of the variables – general grade average, average in the methodological courses, two variables measuring the attitude toward null hypothesis significance testing, failing at least one methodological course, and the variable of main interest in psychology – were predictive for the odds of answering the items in the NHST test correctly. The conclusion of the study is that education practices in teaching students the meaning and interpretation of null hypothesis significance testing have to be taken under consideration at Croatian psychology departments.

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