article · Evaluation Review · 2020

Overview of Bayesian Statistics

Cited by 9Abstract and details only

Abstract

features several Bayesian contributions. In this overview, I present the basics of Bayesian inference. Bayesian statistics is based on the principle that parameters have a distribution of beliefs about them that behave exactly like probability distributions. We can use Bayes' Theorem to update our beliefs about values of the parameters as new information becomes available. Even better, we can make statements that frequentists do not, such as "the probability that an effect is larger than 0 is .93," and can interpret 95% (e.g.) intervals as people naturally want, that there is a 95% probability that the parameter is in that interval. I illustrate the basic concepts of Bayesian statistics through a simple example of predicting admissions to a PhD program.

Cite this paper

APA 7

Rindskopf, D. (2020). Overview of Bayesian Statistics. Evaluation Review, 44(4), 225–237. https://doi.org/10.1177/0193841x19895623

MLA 9

Rindskopf, David. “Overview of Bayesian Statistics.” Evaluation Review, vol. 44, no. 4, 2020, pp. 225–37, https://doi.org/10.1177/0193841x19895623.

Chicago

Rindskopf, David. 2020. “Overview of Bayesian Statistics.” Evaluation Review 44 (4): 225–37. https://doi.org/10.1177/0193841x19895623.

Harvard

Rindskopf, D. (2020) ‘Overview of Bayesian Statistics’, Evaluation Review, 44(4), pp. 225–237. Available at: https://doi.org/10.1177/0193841x19895623.

BibTeX

@article{rindskopf2020overview,
  title = {{Overview of Bayesian Statistics}},
  author = {Rindskopf, David},
  journal = {Evaluation Review},
  volume = {44},
  number = {4},
  pages = {225--237},
  year = {2020},
  doi = {10.1177/0193841x19895623}
}

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