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Jorge N. Tendeiro; Rink Hoekstra; Tsz Keung Wong; Henk A. L. Kiers – Teaching Statistics: An International Journal for Teachers, 2025
Most researchers receive formal training in frequentist statistics during their undergraduate studies. In particular, hypothesis testing is usually rooted on the null hypothesis significance testing paradigm and its p-value. Null hypothesis Bayesian testing and its so-called Bayes factor are now becoming increasingly popular. Although the Bayes…
Descriptors: Statistics Education, Teaching Methods, Programming Languages, Bayesian Statistics
Rosa W. Runhardt – Sociological Methods & Research, 2024
This article uses the interventionist theory of causation, a counterfactual theory taken from philosophy of science, to strengthen causal analysis in process tracing research. Causal claims from process tracing are re-expressed in terms of so-called hypothetical interventions, and concrete evidential tests are proposed which are shown to…
Descriptors: Causal Models, Statistical Inference, Intervention, Investigations
Xiao Liu; Zhiyong Zhang; Lijuan Wang – Grantee Submission, 2022
Mediation analysis is widely used to study whether the effect of an independent variable on an outcome is transmitted through a mediator. Bayesian methods have become increasingly popular for mediation analysis. However, limited research has been done on formal Bayesian hypothesis testing of mediation. Although hypothesis testing using Bayes…
Descriptors: Bayesian Statistics, Hypothesis Testing, Mediation Theory, Vignettes
Xiao Liu; Zhiyong Zhang; Lijuan Wang – Grantee Submission, 2024
In psychology, researchers are often interested in testing hypotheses about mediation, such as testing the presence of a mediation effect of a treatment (e.g., intervention assignment) on an outcome via a mediator. An increasingly popular approach to testing hypotheses is the Bayesian testing approach with Bayes factors (BFs). Despite the growing…
Descriptors: Sample Size, Bayesian Statistics, Programming Languages, Simulation
Frank Wang – Numeracy, 2021
In late November 2020, there was a flurry of media coverage of two companies' claims of 95% efficacy rates of newly developed COVID-19 vaccines, but information about the confidence interval was not reported. This paper presents a way of teaching the concept of hypothesis testing and the construction of confidence intervals using numbers announced…
Descriptors: COVID-19, Pandemics, Immunization Programs, Hypothesis Testing
Brauer, Jonathan R.; Day, Jacob C.; Hammond, Brittany M. – Sociological Methods & Research, 2021
This article presents two alternative methods to null hypothesis significance testing (NHST) for improving inferences from underpowered research designs. Post hoc design analysis (PHDA) assesses whether an NHST analysis generating null findings might otherwise have had sufficient power to detect effects of plausible magnitudes. Bayesian analysis…
Descriptors: Hypothesis Testing, Statistical Analysis, Bayesian Statistics, Statistical Significance
Mani, Nivedita; Schreiner, Melanie S.; Brase, Julia; Köhler, Katrin; Strassen, Katrin; Postin, Danilo; Schultze, Thomas – Developmental Science, 2021
Developmental research, like many fields, is plagued by low sample sizes and inconclusive findings. The problem is amplified by the difficulties associated with recruiting infant participants for research as well as the increased variability in infant responses. With sequential testing designs providing a viable alternative to paradigms facing…
Descriptors: Bayesian Statistics, Infants, Language Acquisition, Vocabulary
Austerweil, Joseph L.; Sanborn, Sophia; Griffiths, Thomas L. – Cognitive Science, 2019
Generalization is a fundamental problem solved by every cognitive system in essentially every domain. Although it is known that how people generalize varies in complex ways depending on the context or domain, it is an open question how people "learn" the appropriate way to generalize for a new context. To understand this capability, we…
Descriptors: Generalization, Logical Thinking, Inferences, Bayesian Statistics
Brydges, Christopher R.; Gaeta, Laura – Journal of Speech, Language, and Hearing Research, 2019
Purpose: Null hypothesis significance testing is commonly used in audiology research to determine the presence of an effect. Knowledge of study outcomes, including nonsignificant findings, is important for evidence-based practice. Nonsignificant "p" values obtained from null hypothesis significance testing cannot differentiate between…
Descriptors: Bayesian Statistics, Audiology, Hypothesis Testing, Statistical Significance
Sinharay, Sandip; Johnson, Matthew S. – Grantee Submission, 2019
According to Wollack and Schoenig (2018), score differencing is one of six types of statistical methods used to detect test fraud. In this paper, we suggested the use of Bayes factors (e.g., Kass & Raftery, 1995) for score differencing. A simulation study shows that the suggested approach performs slightly better than an existing frequentist…
Descriptors: Cheating, Deception, Statistical Analysis, Bayesian Statistics
Held, Leonhard; Matthews, Robert; Ott, Manuela; Pawel, Samuel – Research Synthesis Methods, 2022
It is now widely accepted that the standard inferential toolkit used by the scientific research community--null-hypothesis significance testing (NHST)--is not fit for purpose. Yet despite the threat posed to the scientific enterprise, there is no agreement concerning alternative approaches for evidence assessment. This lack of consensus reflects…
Descriptors: Bayesian Statistics, Statistical Inference, Hypothesis Testing, Credibility
Miocevic, Milica; Klaassen, Fayette; Geuke, Gemma; Moeyaert, Mariola; Maric, Marija – Grantee Submission, 2020
Single-Case Experimental Designs (SCEDs) have lately been recognized as a valuable alternative tolarge group studies. SCEDs form a great tool for the evaluation of treatment effectiveness in heterogeneous and low-incidence conditions, which are common in the field of communication disorders. Mediation analysis is indispensable in treatment…
Descriptors: Bayesian Statistics, Computation, Intervention, Case Studies
Brydges, Christopher R.; Gaeta, Laura – Journal of Speech, Language, and Hearing Research, 2019
Purpose: Evidence-based data analysis methods are important in clinical research fields, including speech-language pathology and audiology. Although commonly used, null hypothesis significance testing (NHST) has several limitations with regard to the conclusions that can be drawn from results, particularly nonsignificant findings. Bayes factors…
Descriptors: Bayesian Statistics, Statistical Analysis, Speech Language Pathology, Audiology
Norouzian, Reza; de Miranda, Michael; Plonsky, Luke – Modern Language Journal, 2019
Null hypothesis testing has long since been the 'go-to analytic approach' in quantitative second language (L2) research (Norris, 2015, p. 97). To many, however, years of reliance on this approach has resulted in a crisis of inference across the social and behavioral sciences (e.g., Rouder et al., 2016). As an alternative to the null hypothesis…
Descriptors: Bayesian Statistics, Second Language Learning, Second Language Instruction, Hypothesis Testing
Patriota, Alexandre Galvão – Educational and Psychological Measurement, 2017
Bayesian and classical statistical approaches are based on different types of logical principles. In order to avoid mistaken inferences and misguided interpretations, the practitioner must respect the inference rules embedded into each statistical method. Ignoring these principles leads to the paradoxical conclusions that the hypothesis…
Descriptors: Hypothesis Testing, Bayesian Statistics, Statistical Inference, Statistical Analysis

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