Bayesian t tests for accepting and rejecting the null hypothesis
Impact in
Classified as
- Journal
- Psychonomic Bulletin & Review
In The Last Decade
doi.org/10.3758/pbr.16.2.225 →Countries where authors are citing Bayesian t tests for accepting and rejecting the null hypothesis
This map shows the geographic impact of Bayesian t tests for accepting and rejecting the null hypothesis. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Bayesian t tests for accepting and rejecting the null hypothesis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bayesian t tests for accepting and rejecting the null hypothesis more than expected).
Fields of papers citing Bayesian t tests for accepting and rejecting the null hypothesis
This network shows the impact of Bayesian t tests for accepting and rejecting the null hypothesis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Bayesian t tests for accepting and rejecting the null hypothesis.
About Bayesian t tests for accepting and rejecting the null hypothesis
This paper, published in 2009, received 2.8k indexed citations . Written by Jeffrey N. Rouder, Paul L. Speckman, Dongchu Sun, Richard Donald Morey and Geoffrey Iverson covering the research area of Statistics, Probability and Uncertainty, Statistics and Probability and General Decision Sciences. It is primarily cited by scholars working on Cognitive Neuroscience (1.6k citations), Experimental and Cognitive Psychology (586 citations), Social Psychology (486 citations), Developmental and Educational Psychology (421 citations) and Statistics and Probability (267 citations). Published in Psychonomic Bulletin & Review.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.3758/pbr.16.2.225.