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What to believe: Bayesian methods for data analysis
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DOI：10.1016/j.tics.2010.05.001.png)
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Abstract
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Although Bayesian models of mind have attracted great interest from cognitive scientists, Bayesian methods for data analysis have not. This article reviews several advantages of Bayesian data analysis over traditional null-hypothesis significance testing. Bayesian methods provide tremendous flexibility for data analytic models and yield rich information about parameters that can be used cumulatively across progressive experiments. Because Bayesian statistical methods can be applied to any data, regardless of the type of cognitive model (Bayesian or otherwise) that motivated the data collection, Bayesian methods for data analysis will continue to be appropriate even if Bayesian models of mind lose their appeal.
Keywords:
SAMPLE-SIZE DETERMINATION
MODELING INDIVIDUAL-DIFFERENCES
STATISTICAL-INFERENCE
SOFTWARE
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