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Hans Humenberger – Teaching Statistics: An International Journal for Teachers, 2025
In the last years special "ovals" appear increasingly often in diagrams and applets for discussing crucial items of statistical inference (when dealing with confidence intervals for an unknown probability p; approximation of the binomial distribution by the normal distribution; especially in German literature, see e.g. [Meyer,…
Descriptors: Computer Oriented Programs, Prediction, Intervals, Statistical Inference
van Doorn, Johnny; Matzke, Dora; Wagenmakers, Eric-Jan – Psychology Learning and Teaching, 2020
Sir Ronald Fisher's venerable experiment "The Lady Tasting Tea" is revisited from a Bayesian perspective. We demonstrate how a similar tasting experiment, conducted in a classroom setting, can familiarize students with several key concepts of Bayesian inference, such as the prior distribution, the posterior distribution, the Bayes…
Descriptors: Bayesian Statistics, Statistical Inference, Statistical Distributions, Sequential Approach
Carpenter, Bob; Gelman, Andrew; Hoffman, Matthew D.; Lee, Daniel; Goodrich, Ben; Betancourt, Michael; Brubaker, Marcus A.; Guo, Jiqiang; Li, Peter; Riddell, Allen – Grantee Submission, 2017
Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the…
Descriptors: Programming Languages, Probability, Bayesian Statistics, Monte Carlo Methods
O'Hara, Michael E. – Journal of Economic Education, 2014
Although the concept of the sampling distribution is at the core of much of what we do in econometrics, it is a concept that is often difficult for students to grasp. The thought process behind bootstrapping provides a way for students to conceptualize the sampling distribution in a way that is intuitive and visual. However, teaching students to…
Descriptors: Economics Education, Economics, Sampling, Statistical Inference
Laumakis, Paul – Mathematics Teacher, 2011
When taking mathematics courses, students will sometimes ask their recurring question, "When will I ever use this in real life?" Educators are often unable to provide a direct connection between what they are teaching in the classroom and a real-life application. However, when such an opportunity does arise, they should seize it and…
Descriptors: Regression (Statistics), Mathematics Instruction, Mathematics, Mathematics Curriculum
Rossman, Allan J. – Statistics Education Research Journal, 2008
This paper identifies key concepts and issues associated with the reasoning of informal statistical inference. I focus on key ideas of inference that I think all students should learn, including at secondary level as well as tertiary. I argue that a fundamental component of inference is to go beyond the data at hand, and I propose that statistical…
Descriptors: Statistical Inference, Probability, Sampling, Statistical Distributions
Konold, Cliff; Harradine, Anthony; Kazak, Sibel – International Journal of Computers for Mathematical Learning, 2007
In current curriculum materials for middle school students in the US, data and chance are considered as separate topics. They are then ideally brought together in the minds of high school or university students when they learn about statistical inference. In recent studies we have been attempting to build connections between data and chance in the…
Descriptors: Middle School Students, Computer Software, Statistical Inference, Statistical Distributions