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Lübke, Karsten; Gehrke, Matthias; Horst, Jörg; Szepannek, Gero – Journal of Statistics Education, 2020
Basic knowledge of ideas of causal inference can help students to think beyond data, that is, to think more clearly about the data generating process. Especially for (maybe big) observational data, qualitative assumptions are important for the conclusions drawn and interpretation of the quantitative results. Concepts of causal inference can also…
Descriptors: Inferences, Simulation, Attribution Theory, Teaching Methods
Beemer, Joshua; Spoon, Kelly; Fan, Juanjuan; Stronach, Jeanne; Frazee, James P.; Bohonak, Andrew J.; Levine, Richard A. – Journal of Statistics Education, 2018
Estimating the efficacy of different instructional modalities, techniques, and interventions is challenging because teaching style covaries with instructor, and the typical student only takes a course once. We introduce the individualized treatment effect (ITE) from analyses of personalized medicine as a means to quantify individual student…
Descriptors: Learning Modalities, Academic Achievement, Intervention, Educational Research
Dunn, Peter K. – Journal of Statistics Education, 2013
In this paper, we report a case study that illustrates the importance in interpreting the results from statistical tests, and shows the difference between practical importance and statistical significance. This case study presents three sets of data concerning the performance of two brands of batteries. The data are easy to describe and…
Descriptors: Equipment, Performance, Statistical Analysis, Statistical Significance
Casleton, Emily; Beyler, Amy; Genschel, Ulrike; Wilson, Alyson – Journal of Statistics Education, 2014
Undergraduate students who have just completed an introductory statistics course often lack deep understanding of variability and enthusiasm for the field of statistics. This paper argues that by introducing the commonly underemphasized concept of measurement error, students will have a better chance of attaining both. We further present lecture…
Descriptors: Undergraduate Students, Statistics, Measurement Techniques, Error of Measurement
Cooper, Linda L.; Shore, Felice S. – Journal of Statistics Education, 2010
Recognizing and interpreting variability in data lies at the heart of statistical reasoning. Since graphical displays should facilitate communication about data, statistical literacy should include an understanding of how variability in data can be gleaned from a graph. This paper identifies several types of graphs that students typically…
Descriptors: Graphs, Charts, Statistics, Visualization
Pfannkuch, Maxine; Regan, Matt; Wild, Chris; Horton, Nicholas J. – Journal of Statistics Education, 2010
Language and the telling of data stories have fundamental roles in advancing the GAISE agenda of shifting the emphasis in statistics education from the operation of sets of procedures towards conceptual understanding and communication. In this paper we discuss some of the major issues surrounding story telling in statistics, challenge current…
Descriptors: Foreign Countries, Schemata (Cognition), Statistics, Mathematics Instruction
Carnell, Lisa J. – Journal of Statistics Education, 2008
Students often enter an introductory statistics class with less than positive attitudes about the subject. They tend to believe statistics is difficult and irrelevant to their lives. Observational evidence from previous studies suggests including projects in a statistics course may enhance students' attitudes toward statistics. This study examines…
Descriptors: Student Attitudes, Data Collection, Statistics, Introductory Courses