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Ravi Selker

Bio: Ravi Selker is an academic researcher from University of Amsterdam. The author has contributed to research in topics: Bayes factor & Statistical hypothesis testing. The author has an hindex of 8, co-authored 11 publications receiving 1734 citations.

Papers
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Journal ArticleDOI
TL;DR: This part of this series introduces JASP (http://www.jasp-stats.org), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems.
Abstract: Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP (http://www.jasp-stats.org), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder’s BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.

1,031 citations

Journal ArticleDOI
TL;DR: Ten prominent advantages of the Bayesian approach are outlined, and several objections to Bayesian hypothesis testing are countered.
Abstract: Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete opportunities for pragmatic researchers. For instance, Bayesian hypothesis testing allows researchers to quantify evidence and monitor its progression as data come in, without needing to know the intention with which the data were collected. We end by countering several objections to Bayesian hypothesis testing. Part II of this series discusses JASP, a free and open source software program that makes it easy to conduct Bayesian estimation and testing for a range of popular statistical scenarios (Wagenmakers et al. this issue).

940 citations

Journal ArticleDOI
TL;DR: This paper introduces JASP, a free graphical software package for basic statistical procedures such as t tests, ANOVAs, linear regression models, and analyses of contingency tables that provides some of the recent developments in Bayesian hypothesis testing and Bayesian parameter estimation.
Abstract: This paper introduces JASP, a free graphical software package for basic statistical procedures such as t tests, ANOVAs, linear regression models, and analyses of contingency tables. JASP is open-source and differentiates itself from existing open-source solutions in two ways. First, JASP provides several innovations in user interface design; specifically, results are provided immediately as the user makes changes to options, output is attractive, minimalist, and designed around the principle of progressive disclosure, and analyses can be peer reviewed without requiring a “syntax”. Second, JASP provides some of the recent developments in Bayesian hypothesis testing and Bayesian parameter estimation. The ease with which these relatively complex Bayesian techniques are available in JASP encourages their broader adoption and furthers a more inclusive statistical reporting practice. The JASP analyses are implemented in R and a series of R packages. © 2019, American Statistical Association. All rights reserved.

354 citations

Journal ArticleDOI
TL;DR: The results of a preregistered replication attempt of Experiment 2 from Topolinski and Sparenberg (2012) show that the effect went slightly in the direction opposite to that predicted, and a pre registered Bayes factor hypothesis test revealed that the data were 10.76 times more likely under the null hypothesis than under the alternative hypothesis.
Abstract: In a series of four experiments, Topolinski and Sparenberg (2012) found support for the conjecture that clockwise movements induce psychological states of temporal progression and an orientation toward the future and novelty. Here we report the results of a preregistered replication attempt of Experiment 2 from Topolinski and Sparenberg (2012). Participants turned kitchen rolls either clockwise or counterclockwise while answering items from a questionnaire assessing openness to experience. Data from 102 participants showed that the effect went slightly in the direction opposite to that predicted by Topolinski and Sparenberg (2012), and a preregistered Bayes factor hypothesis test revealed that the data were 10.76 times more likely under the null hypothesis than under the alternative hypothesis. Our findings illustrate the theoretical importance and practical advantages of preregistered Bayes factor replication studies, both for psychological science and for empirical work in general.

42 citations

Journal Article
TL;DR: This work introduces JASP, an graphical environment for statistical analyses that combines the strength of R, open-source and state-of-the-art statistical methods, with the flexibility and accessibility of proprietary statistical software.
Abstract: While there is a call for the use of novel statistical inference methods in the social sciences, the scientific community is slow in adapting to using such statistical methods. A possible reason for this is that state-of-the-art statistical methods are mostly implemented in the statistical programming language R whereas empirical social scientists more often rely on propitiatory statistical software such as SPSS. We introduce JASP, an graphical environment for statistical analyses that combines the strength of R, open-source and state-of-the-art statistical methods, with the flexibility and accessibility of proprietary statistical software. JASP provides a rich graphical user interface and implements a wide range of classical as well as Bayesian analyses. JASP is freely available for Windows, Mac OS X and Linux under an open source license from https://jasp-stats.org.

40 citations


Cited by
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1,484 citations

Journal ArticleDOI
TL;DR: This part of this series introduces JASP (http://www.jasp-stats.org), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems.
Abstract: Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP (http://www.jasp-stats.org), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder’s BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.

1,031 citations

Journal ArticleDOI
TL;DR: Ten prominent advantages of the Bayesian approach are outlined, and several objections to Bayesian hypothesis testing are countered.
Abstract: Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete opportunities for pragmatic researchers. For instance, Bayesian hypothesis testing allows researchers to quantify evidence and monitor its progression as data come in, without needing to know the intention with which the data were collected. We end by countering several objections to Bayesian hypothesis testing. Part II of this series discusses JASP, a free and open source software program that makes it easy to conduct Bayesian estimation and testing for a range of popular statistical scenarios (Wagenmakers et al. this issue).

940 citations

Journal ArticleDOI
TL;DR: It is found that peer beliefs of replicability are strongly related to replicable, suggesting that the research community could predict which results would replicate and that failures to replicate were not the result of chance alone.
Abstract: Being able to replicate scientific findings is crucial for scientific progress. We replicate 21 systematically selected experimental studies in the social sciences published in Nature and Science between 2010 and 2015. The replications follow analysis plans reviewed by the original authors and pre-registered prior to the replications. The replications are high powered, with sample sizes on average about five times higher than in the original studies. We find a significant effect in the same direction as the original study for 13 (62%) studies, and the effect size of the replications is on average about 50% of the original effect size. Replicability varies between 12 (57%) and 14 (67%) studies for complementary replicability indicators. Consistent with these results, the estimated true-positive rate is 67% in a Bayesian analysis. The relative effect size of true positives is estimated to be 71%, suggesting that both false positives and inflated effect sizes of true positives contribute to imperfect reproducibility. Furthermore, we find that peer beliefs of replicability are strongly related to replicability, suggesting that the research community could predict which results would replicate and that failures to replicate were not the result of chance alone.

759 citations

Journal ArticleDOI
TL;DR: The Bayesian framework for statistics is quickly gaining in popularity among scientists, for reasons such as reliability and accuracy, the possibility of incorporating prior knowledge into the analysis, and the intuitive interpretation of results.
Abstract: The Bayesian framework for statistics is quickly gaining in popularity among scientists, for reasons such as reliability and accuracy (particularly in noisy data and small samples), the possibility of incorporating prior knowledge into the analysis, and the intuitive interpretation of results (Andrews & Baguley, 2013; Etz & Vandekerckhove, 2016; Kruschke, 2010; Kruschke, Aguinis, & Joo, 2012; Wagenmakers et al., 2017). Adopting the Bayesian framework is more of a shift in the paradigm than a change in the methodology; all the common statistical procedures (t-tests, correlations, ANOVAs, regressions, etc.) can also be achieved within the Bayesian framework. One of the core difference is that in the frequentist view, the effects are fixed (but unknown) and data are random. On the other hand, instead of having single estimates of the “true effect”, the Bayesian inference process computes the probability of different effects given the observed data, resulting in a distribution of possible values for the parameters, called the posterior distribution. The bayestestR package provides tools to describe these posterior distributions.

505 citations