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Pfannkuch, Maxine; Forbes, Sharleen; Harraway, John; Budgett, Stephanie; Wild, Chris – Teaching and Learning Research Initiative, 2013
This report summarizes the research activities and findings from the Teaching and Learning Research Initiative (TLRI)-funded project conducted in Year 13, introductory university and workplace classes, entitled "'Bootstrapping' Statistical Inferential Reasoning". The project was a 2-year collaboration among three statisticians, two…
Descriptors: Sampling, Statistical Inference, Higher Education, Workplace Learning
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Buchanan, Taylor L.; Lohse, Keith R. – Measurement in Physical Education and Exercise Science, 2016
We surveyed researchers in the health and exercise sciences to explore different areas and magnitudes of bias in researchers' decision making. Participants were presented with scenarios (testing a central hypothesis with p = 0.06 or p = 0.04) in a random order and surveyed about what they would do in each scenario. Participants showed significant…
Descriptors: Researchers, Attitudes, Statistical Significance, Bias
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Adams, Danielle R.; Meyers, Steven A.; Beidas, Rinad S. – Journal of American College Health, 2016
Objective: Financial strain may directly or indirectly (i.e., through perceived stress) impact students' psychological symptoms and academic and social integration, yet few studies have tested these relationships. The authors explored the mediating effect of perceived stress on the relationship between financial strain and 2 important outcomes:…
Descriptors: Higher Education, Undergraduate Students, First Generation College Students, Mental Health
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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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Ojeda, Mario Miguel; Sahai, Hardeo – International Journal of Mathematical Education in Science and Technology, 2002
Discusses some key statistical concepts in probabilistic and non-probabilistic sampling to provide an overview for understanding the inference process. Suggests a statistical model constituting the basis of statistical inference and provides a brief review of the finite population descriptive inference and a quota sampling inferential theory.…
Descriptors: Educational Strategies, Higher Education, Mathematics Education, Probability
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Wybraniec, John; Wilmoth, Janet – Teaching Sociology, 1999
Discusses the extent to which the existing literature offers insights into effectively teaching statistical inference. Describes an in-class exercise that helps students understand inferential statistics. Explains that students learn more about concepts such as population distribution, sampling distributions, and standard error of the estimate.…
Descriptors: Active Learning, Educational Benefits, Educational Strategies, Higher Education
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Christensen, John O. – Journal of Library Administration, 1988
Description of common errors found in the statistical methodologies of research carried out by librarians, focuses on sampling and generalizability. The discussion covers the need to either adapt library research to the statistical abilities of librarians or to educate librarians in the proper use of statistics. (15 references) (CLB)
Descriptors: Educational Needs, Generalizability Theory, Higher Education, Library Education
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Caulkins, Jonathan P. – Journal of Policy Analysis and Management, 2002
In this article, the author discusses the use in policy analysis of models that incorporate uncertainty. He believes that all models should consider incorporating uncertainty, but that at the same time it is important to understand that sampling variability is not usually the dominant driver of uncertainty in policy analyses. He also argues that…
Descriptors: Statistical Inference, Models, Policy Analysis, Sampling