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Charles G. Minard – Teaching Statistics: An International Journal for Teachers, 2025
Controlling Type 1 error and encouraging reproducible research are important in clinical and translational research. These concepts are frequently discussed in lectures with mathematical language, analytic examples, and probability distributions that demonstrate the issues. However, first-time learners in biostatistics courses focusing on…
Descriptors: Statistics Education, Error Patterns, Probability, Demonstrations (Educational)
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Ruth V. Walker; Hannah Osborn; Julie Madden; Kristen Jennings Black – Teaching of Psychology, 2025
Introduction: In an increasingly diverse world, there has been a call for psychology educators to make efforts to integrate diversity into the psychology curriculum. Statement of the Problem: Researchers who have surveyed psychology faculty have found the amount of time devoted to diversity content in nondiversity-focused courses is limited, with…
Descriptors: Psychology, Statistics Education, Diversity, Course Content
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David Voas; Laura Watt – Teaching Statistics: An International Journal for Teachers, 2025
Binary logistic regression is one of the most widely used statistical tools. The method uses odds, log odds, and odds ratios, which are difficult to understand and interpret. Understanding of logistic regression tends to fall down in one of three ways: (1) Many students and researchers come to believe that an odds ratio translates directly into…
Descriptors: Statistics, Statistics Education, Regression (Statistics), Misconceptions
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Pearl, Dennis K.; Lesser, Lawrence M. – Teaching Statistics: An International Journal for Teachers, 2023
Concepts of experimentation and measurement are explored using statistics educational fun items and illustrated by sharing our process in conducting an experiment on cartoon captions.
Descriptors: Statistics Education, Experiments, Measurement, Cartoons
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Aslihan Batur Ozturk; Adnan Baki – Pedagogical Research, 2024
Since it has become necessary for each individual to be statistically literate, statistical education research has taken teachers' competencies into its agenda. The knowledge needed to teach statistics differs from the knowledge needed to teach mathematics since statistics is different from mathematics. Teachers and researchers need to consider…
Descriptors: Statistics Education, Teaching Models, Pedagogical Content Knowledge, Teacher Education
Weihao Wang – ProQuest LLC, 2024
In this work, we introduce a novel oversampling technique, the theory of inheritance and Gower distance-based oversampling (TIGO) method, designed to address class imbalance issues in mixed categorical and continuous variables data set. Drawing inspiration from genetic inheritance principles, TIGO synthesizes new minority class data,…
Descriptors: Sampling, Statistics Education, Data Analysis, Prediction
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J. S. Allison; L. Santana; I. J. H. Visagie – Teaching Statistics: An International Journal for Teachers, 2025
Given sample data, how do you calculate the value of a parameter? While this question is impossible to answer, it is frequently encountered in statistics classes when students are introduced to the distinction between a sample and a population (or between a statistic and a parameter). It is not uncommon for teachers of statistics to also confuse…
Descriptors: Statistics Education, Teaching Methods, Computation, Sampling
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Marci S. DeCaro; Campbell R. Bego; Lianda Velic; Phillip M. Newman – Instructional Science: An International Journal of the Learning Sciences, 2025
Instructors traditionally lecture on new content before providing practice problems, but learning is often superficial. Exploratory learning before instruction deepens conceptual understanding by giving students a novel activity to explore before direct instruction. We examined how increasing the salience of contrasting cases in exploration versus…
Descriptors: Discovery Learning, Undergraduate Students, Statistics Education, Learning Activities
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López-Barrientos, José Daniel; Silva, Eliud; Lemus-Rodríguez, Enrique – Teaching Statistics: An International Journal for Teachers, 2023
We take advantage of a combinatorial misconception and the famous paradox of the Chevalier de Méré to present the multiplication rule for independent events; the principle of inclusion and exclusion in the presence of disjoint events; the median of a discrete-type random variable, and a confidence interval for a large sample. Moreover, we pay…
Descriptors: Statistics Education, Mathematical Concepts, Multiplication, Misconceptions
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Allison Davidson – Journal of Statistics and Data Science Education, 2024
An investigative project can engage the learner in all aspects of a statistical investigation, including developing a question or issue of interest, gathering needed information, exploring the data, and communicating the results. This article summarizes the available literature regarding the implementation of investigative projects, including the…
Descriptors: Student Projects, Active Learning, Statistics Education, Educational Benefits
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Jitu Halomoan Lumbantoruan – REDIMAT - Journal of Research in Mathematics Education, 2024
The aim of the present exploratory study was to examine students' situational engagement and motivation in the statistics classroom at Zayed University, in Dubai, United Arab Emirates (UAE). Two instruments were used for this purpose: a) experience sampling method (ESM), and b) the validated Mathematics Motivation Questionnaire (MMQ). This study…
Descriptors: Learner Engagement, Student Motivation, Statistics, Statistics Education
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Jorge N. Tendeiro; Rink Hoekstra; Tsz Keung Wong; Henk A. L. Kiers – Teaching Statistics: An International Journal for Teachers, 2025
Most researchers receive formal training in frequentist statistics during their undergraduate studies. In particular, hypothesis testing is usually rooted on the null hypothesis significance testing paradigm and its p-value. Null hypothesis Bayesian testing and its so-called Bayes factor are now becoming increasingly popular. Although the Bayes…
Descriptors: Statistics Education, Teaching Methods, Programming Languages, Bayesian Statistics
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Rujun Duan; Qi Sun; Xiuhong Tong – npj Science of Learning, 2025
Statistical learning is a core ability for individuals in extracting and integrating regularities and patterns from linguistic input. Yet, the developmental trajectory of visual statistical learning has not been fully examined in the orthographic learning domain. Employing an artificial orthographic learning task, we manipulated three levels of…
Descriptors: Statistics Education, Linguistic Input, Visual Aids, Orthographic Symbols
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Beth Chance; Karen McGaughey; Sophia Chung; Alex Goodman; Soma Roy; Nathan Tintle – Journal of Statistics and Data Science Education, 2025
"Simulation-based inference" is often considered a pedagogical strategy for helping students develop inferential reasoning, for example, giving them a visual and concrete reference for deciding whether the observed statistic is unlikely to happen by chance alone when the null hypothesis is true. In this article, we highlight for teachers…
Descriptors: Simulation, Sampling, Randomized Controlled Trials, Hypothesis Testing
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Fariba Nosrati; Timothy Burns; Yuan Gao; Cherie Sherman – Information Systems Education Journal, 2025
The purpose of this study is to investigate the current state of graduate level business analytics education in the United States. The goal of this research is twofold. The first goal is to understand how higher education institutions are addressing the growing demand for analysts and data-savvy managers in the job market. To achieve this aim, the…
Descriptors: Graduate Students, Data Analysis, Statistics Education, Labor Needs
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