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Jones, Ryan Seth; Jia, Zhigang; Bezaire, Joel – Mathematics Teacher: Learning and Teaching PK-12, 2020
Too often, statistical inference and probability are treated in schools like they are unrelated. In this paper, we describe how we supported students to learn about the role of probability in making inferences with variable data by building models of real world events and using them to simulate repeated samples.
Descriptors: Statistical Inference, Probability, Mathematics Instruction, Mathematical Models
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Strayer, Jeremy; Matuszewski, Amber – Mathematics Teacher, 2016
In this article, Strayer and Matuszewski present a six-phase strategy that teachers can use to help students develop a conceptual understanding of inferential hypothesis testing through simulation. As Strayer and Matuszewski discuss the strategy, they describe each phase in general, explain how they implemented the phase while teaching their…
Descriptors: Mathematics Instruction, Teaching Methods, Hypothesis Testing, Simulation
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Case, Catherine; Whitaker, Douglas – Mathematics Teacher, 2016
In the criminal justice system, defendants accused of a crime are presumed innocent until proven guilty. Statistical inference in any context is built on an analogous principle: The null hypothesis--often a hypothesis of "no difference" or "no effect"--is presumed true unless there is sufficient evidence against it. In this…
Descriptors: Mathematics Instruction, Technology Uses in Education, Educational Technology, Statistical Inference
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Calzada, Maria E.; Gardner, Holly – Mathematics and Computer Education, 2011
The results of a simulation conducted by a research team involving undergraduate and high school students indicate that when data is symmetric the student's "t" confidence interval for a mean is superior to the studied non-parametric bootstrap confidence intervals. When data is skewed and for sample sizes n greater than or equal to 10,…
Descriptors: Intervals, Effect Size, Simulation, Undergraduate Students
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Mulekar, Madhuri S.; Siegel, Murray H. – Mathematics Teacher, 2009
If students are to understand inferential statistics successfully, they must have a profound understanding of the nature of the sampling distribution. Specifically, they must comprehend the determination of the expected value and standard error of a sampling distribution as well as the meaning of the central limit theorem. Many students in a high…
Descriptors: Statistical Inference, Statistics, Sample Size, Error of Measurement
Johnson, H. Dean; Evans, Marc A. – Australian Mathematics Teacher, 2008
Understanding the concept of the sampling distribution of a statistic is essential for the understanding of inferential procedures. Unfortunately, this topic proves to be a stumbling block for students in introductory statistics classes. In efforts to aid students in their understanding of this concept, alternatives to a lecture-based mode of…
Descriptors: Class Activities, Intervals, Computer Software, Sampling
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Moen, David H.; Powell, John E. – American Journal of Business Education, 2008
Using Microsoft® Excel, several interactive, computerized learning modules are developed to illustrate the Central Limit Theorem's appropriateness for comparing the difference between the means of any two populations. These modules are used in the classroom to enhance the comprehension of this theorem as well as the concepts that provide the…
Descriptors: Learning Modules, Computer Simulation, Classroom Techniques, Concept Teaching
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Derry, Sharon; And Others – Teaching of Psychology, 1995
Maintains that literacy and informed decision making in an uncertain world require the ability to reason statistically. Describes a course designed to help students use statistical concepts as tools for social reasoning within simulations of real-world problems. Describes four types of instructional activities used. (CFR)
Descriptors: Cognitive Processes, Course Content, Course Descriptions, Critical Thinking