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Integrated statistical inference: the amalgamation of conventional and Bayesian statistical inference in introductory statistics courses

James Baglin, Caterina Costa

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Abstract

Many educators of statistics have considered the idea of introducing Bayesian statistical inference into so called 'Bayes for Beginners' courses. There are also many others who have cautioned against doing so, citing the widespread acceptance of conventional statistical methods as a reason to hesitate. A good compromise would see both methods being taught in an integrated fashion to give a student the best of both inferential worlds. This paper will briefly overview Integrated Statistical Inference (ISI), a method for delivering both Bayesian and conventional concepts in an introductory statistics course.

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What this paper is about

Many educators of statistics have considered the idea of introducing Bayesian statistical inference into so called 'Bayes for Beginners' courses. There are also many others who have cautioned against doing so, citing the widespread acceptance of conventional statistical methods as a reason to hesitate. A good compromise would see both methods being taught in an integrated fashion to give a student the best of both inferential worlds. This paper will briefly overview Integrated Statistical Inference (ISI), a method for delivering both Bayesian and conventional concepts in an introductory statistics course.

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Available abstract

Many educators of statistics have considered the idea of introducing Bayesian statistical inference into so called 'Bayes for Beginners' courses. There are also many others who have cautioned against doing so, citing the widespread acceptance of conventional statistical methods as a reason to hesitate. A good compromise would see both methods being taught in an integrated fashion to give a student the best of both inferential worlds. This paper will briefly overview Integrated Statistical Inference (ISI), a method for delivering both Bayesian and conventional concepts in an introductory statistics course.

Key concepts: Statistical inference, Bayesian statistics, Fiducial inference, Inference, Computer science, Bayesian inference, Bayes' theorem, Frequentist inference

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