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Mortaza Jamshidian; Parsa Jamshidian – Journal of Statistics and Data Science Education, 2024
Using software to teach statistical inference in introductory courses opens the door for methods and practices that are more conceptually appealing to students. With an increasing number of fields requiring competency in statistics including data science, natural and social sciences, public health and more, it is crucial that we as instructors…
Descriptors: Computer Software, Computer Assisted Instruction, Teaching Methods, Statistics Education
Binder, Karin; Krauss, Stefan; Schmidmaier, Ralf; Braun, Leah T. – Advances in Health Sciences Education, 2021
When physicians are asked to determine the positive predictive value from the a priori probability of a disease and the sensitivity and false positive rate of a medical test (Bayesian reasoning), it often comes to misjudgments with serious consequences. In daily clinical practice, however, it is not only important that doctors receive a tool with…
Descriptors: Clinical Diagnosis, Efficiency, Probability, Bayesian Statistics
Case, Catherine; Battles, Melanie; Jacobbe, Tim – Investigations in Mathematics Learning, 2019
The study presented in this article examined the impact of two simulation-based inference activities on students' understanding of p-values in a second undergraduate statistics course. In the study, students familiar with traditional inference methods used physical and computer simulations to estimate p-values. To examine students' conceptions…
Descriptors: Probability, Computer Simulation, Statistics, Mathematics Instruction
Reaburn, Robyn – Mathematics Education Research Group of Australasia, 2019
Random sampling and random allocation are essential processes in the practice of inferential statistics. These processes ensure that all members of a population are equally likely to be selected, and that all possible allocations in an experiment are equally likely. It is these characteristics that allow the validity of the subsequent calculations…
Descriptors: Statistics, Comprehension, Introductory Courses, College Students
Lugo-Armenta, Jesús Guadalupe; Pino-Fan, Luis Roberto – Education Sciences, 2021
The COVID-19 pandemic generated a new scenario in education, where technological resources mediate teaching and learning processes. This paper presents the development of a virtual teacher training experience aimed at promoting inferential reasoning in practicing and prospective mathematics teachers using inference problems on the Chi-square…
Descriptors: Statistics Education, Statistical Inference, Probability, Thinking Skills
Zhang, Xuemao; Maas, Zoe – International Electronic Journal of Mathematics Education, 2019
The use of computer simulations in the teaching of introductory statistics can help undergraduate students understand difficult or abstract statistics concepts. The free software environment R is a good candidate for computer simulations since it allows users to add additional functionality by defining new functions. In this paper, we illustrate…
Descriptors: Computer Simulation, Teaching Methods, Mathematics Instruction, Probability
Page, Robert; Satake, Eiki – Journal of Education and Learning, 2017
While interest in Bayesian statistics has been growing in statistics education, the treatment of the topic is still inadequate in both textbooks and the classroom. Because so many fields of study lead to careers that involve a decision-making process requiring an understanding of Bayesian methods, it is becoming increasingly clear that Bayesian…
Descriptors: Probability, Bayesian Statistics, Hypothesis Testing, Statistical Inference
Sarafoglou, Alexandra; van der Heijden, Anna; Draws, Tim; Cornelisse, Joran; Wagenmakers, Eric-Jan; Marsman, Maarten – Psychology Learning and Teaching, 2022
Current developments in the statistics community suggest that modern statistics education should be structured holistically, that is, by allowing students to work with real data and to answer concrete statistical questions, but also by educating them about alternative frameworks, such as Bayesian inference. In this article, we describe how we…
Descriptors: Bayesian Statistics, Thinking Skills, Undergraduate Students, Psychology
Zhang, Zhiyong; Zhang, Danyang – Grantee Submission, 2021
Data science has maintained its popularity for about 20 years. This study adopts a bottom-up approach to understand what data science is by analyzing the descriptions of courses offered by the data science programs in the United States. Through topic modeling, 14 topics are identified from the current curricula of 56 data science programs. These…
Descriptors: Statistics Education, Definitions, Course Descriptions, Computer Science Education
White, Patrick; Gorard, Stephen – Statistics Education Research Journal, 2017
Recent concerns about a shortage of capacity for statistical and numerical analysis skills among social science students and researchers have prompted a range of initiatives aiming to improve teaching in this area. However, these projects have rarely re-evaluated the content of what is taught to students and have instead focussed primarily on…
Descriptors: Statistical Inference, Statistics, Teaching Methods, Social Science Research
Zhou, Xiang; Xie, Yu – Sociological Methods & Research, 2016
Since the seminal introduction of the propensity score (PS) by Rosenbaum and Rubin, PS-based methods have been widely used for drawing causal inferences in the behavioral and social sciences. However, the PS approach depends on the ignorability assumption: there are no unobserved confounders once observed covariates are taken into account. For…
Descriptors: Probability, Statistical Inference, Comparative Analysis, Longitudinal Studies
Kazak, Sibel; Pratt, Dave – Statistics Education Research Journal, 2017
This study considers probability models as tools for both making informal statistical inferences and building stronger conceptual connections between data and chance topics in teaching statistics. In this paper, we aim to explore pre-service mathematics teachers' use of probability models for a chance game, where the sum of two dice matters in…
Descriptors: Preservice Teachers, Probability, Mathematical Models, Statistical Inference
Steiner, Peter M.; Cook, Thomas D.; Li, Wei; Clark, M. H. – Journal of Research on Educational Effectiveness, 2015
In observational studies, selection bias will be completely removed only if the selection mechanism is ignorable, namely, all confounders of treatment selection and potential outcomes are reliably measured. Ideally, well-grounded substantive theories about the selection process and outcome-generating model are used to generate the sample of…
Descriptors: Quasiexperimental Design, Bias, Selection, Observation
Zetterqvist, Lena – Teaching Mathematics and Its Applications, 2017
Researchers and teachers often recommend motivating exercises and use of mathematics or statistics software for the teaching of basic courses in probability and statistics. Our courses are given to large groups of engineering students at Lund Institute of Technology. We found that the mere existence of real-life data and technology in a course…
Descriptors: Technology Uses in Education, Alignment (Education), Probability, Statistics
Reaburn, Robyn – Mathematics Education Research Group of Australasia, 2013
An understanding of conditional probability is essential for students of inferential statistics as it is used in Null Hypothesis Tests. Conditional probability is also used in Bayes' theorem, in the interpretation of medical screening tests and in quality control procedures. This study examines the understanding of conditional probability of…
Descriptors: Foreign Countries, Mathematics Instruction, Statistical Inference, Statistics