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Berg, Arthur – Teaching Statistics: An International Journal for Teachers, 2021
The topic of Bayesian updating is explored using standard and non-standard dice as an intuitive and motivating model. Details of calculating posterior probabilities for a discrete distribution are provided, offering a different view to P-values. This article also includes the stars and bars counting technique, a powerful method of counting that is…
Descriptors: Bayesian Statistics, Teaching Methods, Statistics Education, Intuition
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CadwalladerOlsker, Todd – Mathematics Teacher, 2019
Students studying statistics often misunderstand what statistics represent. Some of the most well-known misunderstandings of statistics revolve around null hypothesis significance testing. One pervasive misunderstanding is that the calculated p-value represents the probability that the null hypothesis is true, and that if p < 0.05, there is…
Descriptors: Statistics, Mathematics Education, Misconceptions, Hypothesis Testing
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Pek, Jolynn; Van Zandt, Trisha – Psychology Learning and Teaching, 2020
Statistical thinking is essential to understanding the nature of scientific results as a consumer. Statistical thinking also facilitates thinking like a scientist. Instead of emphasizing a "correct" procedure for data analysis and its outcome, statistical thinking focuses on the process of data analysis. This article reviews frequentist…
Descriptors: Bayesian Statistics, Thinking Skills, Data Analysis, Evaluation Methods
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Satake, Eiki; Vashlishan Murray, Amy – Teaching Statistics: An International Journal for Teachers, 2015
This paper presents a comparison of three approaches to the teaching of probability to demonstrate how the truth table of elementary mathematical logic can be used to teach the calculations of conditional probabilities. Students are typically introduced to the topic of conditional probabilities--especially the ones that involve Bayes' rule--with…
Descriptors: Teaching Methods, Probability, Bayesian Statistics, Mathematical Logic
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Satake, Eiki; Murray, Amy Vashlishan – Journal of Statistics Education, 2014
Although Bayesian methodology has become a powerful approach for describing uncertainty, it has largely been avoided in undergraduate statistics education. Here we demonstrate that one can present Bayes' Rule in the classroom through a hypothetical, yet realistic, legal scenario designed to spur the interests of students in introductory- and…
Descriptors: Bayesian Statistics, College Mathematics, Mathematics Instruction, Statistics
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Rouder, Jeffrey N.; Lu, Jun; Sun, Dongchu; Speckman, Paul; Morey, Richard; Naveh-Benjamin, Moshe – Psychometrika, 2007
The theory of signal detection is convenient for measuring mnemonic ability in recognition memory paradigms. In these paradigms, randomly selected participants are asked to study randomly selected items. In practice, researchers aggregate data across items or participants or both. The signal detection model is nonlinear; consequently, analysis…
Descriptors: Simulation, Recognition (Psychology), Computation, Mnemonics
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Lockwood, J. R.; McCaffrey, Daniel F.; Mariano, Louis T.; Setodji, Claude – Journal of Educational and Behavioral Statistics, 2007
There is increased interest in value-added models relying on longitudinal student-level test score data to isolate teachers' contributions to student achievement. The complex linkage of students to teachers as students progress through grades poses both substantive and computational challenges. This article introduces a multivariate Bayesian…
Descriptors: Urban Schools, Academic Persistence, Reading Achievement, Mathematics Achievement
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Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use