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Why Bayesian Ideas Should Be Introduced in the Statistics Curricula and How to Do So

Andrew Hoegh

Journal of Statistics and Data Science Education2020https://doi.org/10.1080/10691898.2020.1841591article
ABDC B
Weight
0.57

What the paper says

While computing has become an important part of the statistics field, course offerings are still influenced by a legacy of mathematically centric thinking. Due to this legacy, Bayesian ideas are not required for undergraduate degrees and have largely been taught at the graduate level; however, with recent advances in software and emphasis on computational thinking, Bayesian ideas are more accessible. Statistics curricula need to continue to evolve and students at all levels should be taught Bayesian thinking. This article advocates for adding Bayesian ideas for three groups of students: intro-statistics students, undergraduate statistics majors, and graduate student scientists; and furthermore, provides guidance and materials for creating Bayesian-themed courses for these audiences. Supplementary files for this article are available on line.

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https://doi.org/https://doi.org/10.1080/10691898.2020.1841591

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@article{andrew2020,
  title        = {{Why Bayesian Ideas Should Be Introduced in the Statistics Curricula and How to Do So}},
  author       = {Andrew Hoegh},
  journal      = {Journal of Statistics and Data Science Education},
  year         = {2020},
  doi          = {https://doi.org/https://doi.org/10.1080/10691898.2020.1841591},
}

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Why Bayesian Ideas Should Be Introduced in the Statistics Curricula and How to Do So

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Evidence weight

0.57

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.56 × 0.4 = 0.23
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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