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Tim Erickson – Australian Mathematics Education Journal, 2023
This short article continues the exploration of the Common Online Data Analysis Platform (CODAP) and statistics begun in the previous article "Statistical Investigations and CODAP, Part 1: EDA." In Part 2, the author discusses the teaching of statistical inference focusing on activities for the senior secondary years. In particular, the…
Descriptors: Computer Software, Statistics Education, Statistical Inference, Secondary Education
Noll, Jennifer; Kirin, Dana; Clement, Kit; Dolor, Jason – Mathematical Thinking and Learning: An International Journal, 2023
Using simulation approaches when conducting randomization tests for comparing two groups in the context of experimental studies has been promoted as a beneficial approach for supporting student learning of statistical inference. Many researchers have suggested that the data production process in simulations for the randomization test intuitively…
Descriptors: Mathematics Instruction, Thinking Skills, Comparative Analysis, Learning Processes
Kazak, Sibel; Fujita, Taro; Turmo, Manoli Pifarre – Mathematical Thinking and Learning: An International Journal, 2023
In today's age of information, the use of data is very powerful in making informed decisions. Data analytics is a field that is interested in identifying and interpreting trends and patterns within big data to make data-driven decisions. We focus on informal statistical inference and data modeling as a means of developing students' data analytics…
Descriptors: Statistical Inference, Mathematics Skills, Mathematics Instruction, Secondary School Students
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
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
Churchwell, Don Wesley – ProQuest LLC, 2009
This study examined the relationship between STAR Math gains and TCAP composite scores. The purpose of this study was to determine if there was a significant relationship between STAR Math pretest and posttest gains over the course of the 2005-2006 academic year through the use of the STAR Math software program and TCAP math composite scores at…
Descriptors: Student Needs, Mathematics Achievement, Pretests Posttests, Computer Software
Moen, David H.; Powell, John E. – College Teaching Methods & Styles Journal, 2005
Using Microsoft Excel, several interactive, computerized learning modules are developed to demonstrate the Central Limit Theorem. These modules are used in the classroom to enhance the comprehension of this theorem. The Central Limit Theorem is a very important theorem in statistics, and yet because it is not intuitively obvious, statistics…
Descriptors: Spreadsheets, Computer Software, Computer Simulation, Statistics
Garofalo, Joe; Juersivich, Nicole – NCSSSMST Journal, 2007
There is much research that documents what many teachers know, that students struggle with many concepts in probability and statistics. This article presents two sample activities the authors use to help preservice teachers develop ideas about how they can use technology to promote their students' ability to understand mathematics and connect…
Descriptors: Preservice Teachers, Statistical Inference, Sampling, Probability

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