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• W2892433920 abstract "This paper provides a case for using Bayesian data analysis (BDA) to makemore informed claims regarding practical significance of software engineeringresearch. We show that using BDA, here combined with cumulative prospect theory(CPT), is appropriate when a researcher or practitioner wants to make moreexplicit connections between statistical findings and practical significance inempirical software engineering research. To illustrate our point, we provide anexample case using previously published data. We build a multilevel Bayesianmodel for this data, for which we compare the out of sample predictive power.Finally, we use our model to make out of sample predictions while, ultimately,connecting this to practical significance using CPT. Throughout the case thatwe present, we argue that a Bayesian approach is a natural, theoreticallywell-grounded, practical work-flow for data analysis in empirical softwareengineering. By including prior beliefs, assuming parameters are drawn from aprobability distribution, assuming the true value is a random variable foruncertainty intervals, using counter-factual plots for sanity checks,conducting predictive posterior checks, and out of sample predictions, we willbetter understand the phenomenon being studied, while at the same time avoidthe obsession with $p$-values." @default.
• W2892433920 created "2018-10-05" @default.
• W2892433920 creator A5030588511 @default.
• W2892433920 creator A5031470846 @default.
• W2892433920 creator A5063787358 @default.
• W2892433920 date "2018-09-26" @default.
• W2892433920 modified "2023-09-27" @default.
• W2892433920 title "Arguing Practical Significance in Software Engineering Using Bayesian Data Analysis." @default.
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• W2892433920 hasPublicationYear "2018" @default.
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