### An Introduction to Bayesian Hypothesis Testing for Management Research

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### Variations on a Bayesian Theme: Comparing Bayesian Models of Referential Reasoning

"... Abstract. Recent developments in Bayesian experimental pragmatics have received much attention. The Rational Speech Act (RSA) model formalizes core concepts of traditional pragmatic theories quantitatively and makes predictions that fit empirical data nicely. In this paper, we analyze the RSA model ..."

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Abstract. Recent developments in Bayesian experimental pragmatics have received much attention. The Rational Speech Act (RSA) model formalizes core concepts of traditional pragmatic theories quantitatively and makes predictions that fit empirical data nicely. In this paper, we analyze the RSA model and its relation to closely related game theoretic approaches, by spelling out its belief, goal and action components. We introduce some alternatives motivated from the game theoretic tradition and compare models incorporating these alternatives systematically to the original RSA model, using Bayesian model comparison, in terms of their ability to predict relevant empirical data. The result suggests that the RSA model could be adapted and extended to improve its predictive power, in particular by taking speaker preferences into account.

### Science Perspectives on Psychological Statistical Evidence in Experimental Psychology

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### The EZ diffusion model provides a powerful test of simple empirical effects

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### Errata: Bayesian Tests to Quantify the Result of a Replication Attempt

"... Abstract Errata: Three problems in the Examples section of the published paper were detected and corrected. • Problem 1: The effect sizes printed in the text were computed assuming a one-sample instead of a two-sample t-test. Change: The effect sizes in the text were changed to match the correct ef ..."

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Abstract Errata: Three problems in the Examples section of the published paper were detected and corrected. • Problem 1: The effect sizes printed in the text were computed assuming a one-sample instead of a two-sample t-test. Change: The effect sizes in the text were changed to match the correct effect sizes in • Problem 2: The studies used to compute the professor priming results (studies 3 and 5 from Shanks et al. • Problem 3: The meta-analysis Bayes factor R code had a bug which assumed equal sample sizes in all studies, which was discovered recently. Changed: The meta-analysis Bayes factors were recomputed and changed in Keywords: Effect Size, Prior Distribution, Bayes Factor. Examples We now apply the above Bayesian t tests to three examples of replication attempts from the literature. These examples cover one-sample and two-sample t tests in which the outcome is replication failure, replication success, and replication ambivalence. The examples also allow us to visualize the prior and posterior distributions for effect size, as

### Supplement for "Bayesian Hypothesis Testing for Single-Subject Designs"

"... This document provides example R code demonstrating how to use the BayesSingleSub package (Section 1), and the technical details for the sampling routines (Section 2). Tutorial for computing de Vries and Morey's Bayes factors Here, we show how to compute the Bayes factors B ar , B trend , B in ..."

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This document provides example R code demonstrating how to use the BayesSingleSub package (Section 1), and the technical details for the sampling routines (Section 2). Tutorial for computing de Vries and Morey's Bayes factors Here, we show how to compute the Bayes factors B ar , B trend , B int , and B t+i , and how to obtain and plot the posterior distributions of the model parameters. First, download the R statistical environment from http://cran.r-project.org/ and install the BayesSingleSub package using the R command: Then, load the BayesSingleSub package with the library() function: For the purposes of this demonstration, we compute the Bayes factors for the data shown in For convenience, we divide the data before and after the intervention into separate vectors: The logarithm of the JZS+AR Bayes factor B ar can be obtained by using the ttest.Gibbs.AR() function, and the logarithm of the TAR Bayes factors B int , B trend , and B i+t by using the trendtest.Gibbs.AR() function: 1

### Statistical Evidence in Experimental . . .

, 2011

"... Statistical inference in psychology has traditionally relied heavily on p-value significance testing. This approach to drawing conclusions from data, however, has been widely criticized, and two types of remedies have been advocated. The first proposal is to supplement p values with complementary me ..."

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Statistical inference in psychology has traditionally relied heavily on p-value significance testing. This approach to drawing conclusions from data, however, has been widely criticized, and two types of remedies have been advocated. The first proposal is to supplement p values with complementary measures of evidence, such as effect sizes. The second is to replace inference with Bayesian measures of evidence, such as the Bayes factor. The authors provide a practical comparison of p values, effect sizes, and default Bayes factors as measures of statistical evidence, using 855 recently published t tests in psychology. The comparison yields two main results. First, although p values and default Bayes factors almost always agree about what hypothesis is better supported by the data, the measures often disagree about the strength of this support; for 70 % of the data sets for which the p value falls between.01 and.05, the default Bayes factor indicates that the evidence is only anecdotal. Second, effect sizes can provide additional evidence to p values and default Bayes factors. The authors conclude that the Bayesian approach is comparatively prudent, preventing researchers from overestimating the evidence in favor of an effect.

### Research Article Decision Speed Induces Context Effects in Choice

"... Abstract. The context in which a decision occurs can influence the decision-making process in many ways. In the laboratory, this is often evident in the effects of recent decisions. For instance, many experiments combine easy and difficult decisions, such as when word frequency is manipulated in lex ..."

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Abstract. The context in which a decision occurs can influence the decision-making process in many ways. In the laboratory, this is often evident in the effects of recent decisions. For instance, many experiments combine easy and difficult decisions, such as when word frequency is manipulated in lexical decision. The ‘‘blocking effect’ ’ describes how such decisions differ depending on whether the conditions are presented in pure blocks (comprised purely of easy or hard stimuli) or mixed blocks (also known as a ‘‘mixing cost’’). We present a novel extension to these context effects, demonstrating in two experiments that they can be induced using conditions with identical difficulty, but different timing properties. This suggests that explanations of context effects based on task difficulty or error monitoring alone might be insufficient, and suggest a role for decision time. In prior work, we suggested such a hypothesis under the assumption that observers minimize their decision time, subject to an accuracy constraint. Consistent with this explanation, we find that decisions in slower conditions were based on less evidence when they were experienced in mixed compared to pure blocks.

### Journal of Mathematical Psychology

"... This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or sel ..."

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:

### WoMMBAT: A user interface for hierarchical Bayesian estimation of working memory capacity

, 2011

"... # The Author(s) 2011. This article is published with open access at Springerlink.com Abstract The change detection paradigm has become an important tool for researchers studying working memory. Change detection is especially useful for studying visual working memory, because recall paradigms are dif ..."

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# The Author(s) 2011. This article is published with open access at Springerlink.com Abstract The change detection paradigm has become an important tool for researchers studying working memory. Change detection is especially useful for studying visual working memory, because recall paradigms are difficult to employ in the visual modality. Pashler (Perception &