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Arribas, E.; Escobar, I.; Ramirez-Vazquez, R. – International Journal of Mathematical Education in Science and Technology, 2021
In the article 'How Long Is My Toilet Roll--A Simple Exercise in Mathematical Modelling' several models of increasing complexity are introduced and solved to calculate indirectly the length of paper on a toilet-roll. All these results are presented without errors. The authors of this comment believe the error analysis of measurements made in a…
Descriptors: Mathematics Instruction, Teaching Methods, Mathematical Models, Computation
Howe, Roger – ZDM: The International Journal on Mathematics Education, 2019
This paper makes a proposal, from the perspective of a research mathematician interested in mathematics education, for broadening and deepening whole number arithmetic instruction, to make it more relevant for the twenty-first century, in particular, to enable students to deal with large numbers, arguably an essential skill for modern citizenship.…
Descriptors: Number Concepts, Numbers, Error of Measurement, Computation
Dogan, C. Deha – Eurasian Journal of Educational Research, 2017
Background: Most of the studies in academic journals use p values to represent statistical significance. However, this is not a good indicator of practical significance. Although confidence intervals provide information about the precision of point estimation, they are, unfortunately, rarely used. The infrequent use of confidence intervals might…
Descriptors: Sampling, Statistical Inference, Periodicals, Intervals
Arzumanyan, George; Halcoussis, Dennis; Phillips, G. Michael – American Journal of Business Education, 2015
This paper presents the Agresti & Coull "Adjusted Wald" method for computing confidence intervals and margins of error for common proportion estimates. The presented method is easily implementable by business students and practitioners and provides more accurate estimates of proportions particularly in extreme samples and small…
Descriptors: Business Administration Education, Error of Measurement, Error Patterns, Intervals
Kachapova, Farida; Kachapov, Ilias – Journal of Statistics Education, 2010
Two improvements in teaching linear regression are suggested. The first is to include the population regression model at the beginning of the topic. The second is to use a geometric approach: to interpret the regression estimate as an orthogonal projection and the estimation error as the distance (which is minimized by the projection). Linear…
Descriptors: Statistics, Mathematics Instruction, Economics, Teaching Methods
Schochet, Peter Z. – National Center for Education Evaluation and Regional Assistance, 2009
For RCTs of education interventions, it is often of interest to estimate associations between student and mediating teacher practice outcomes, to examine the extent to which the study's conceptual model is supported by the data, and to identify specific mediators that are most associated with student learning. This paper develops statistical power…
Descriptors: Statistical Analysis, Intervention, Teacher Influence, Teaching Methods
Duerdoth, Ian – Physics Education, 2009
The subject of uncertainties (sometimes called errors) is traditionally taught (to first-year science undergraduates) towards the end of a course on statistics that defines probability as the limit of many trials, and discusses probability distribution functions and the Gaussian distribution. We show how to introduce students to the concepts of…
Descriptors: Least Squares Statistics, Probability, College Science, Undergraduate Study
Siegel, Peter – Physics Teacher, 2007
We present a fun activity that can be used to introduce students to error analysis: the M&M game. Students are told to estimate the number of individual candies plus uncertainty in a bag of M&M's. The winner is the group whose estimate brackets the actual number with the smallest uncertainty. The exercise produces enthusiastic discussions and…
Descriptors: Error of Measurement, Educational Games, Science Instruction, Teaching Methods

Rusling, James F. – Journal of Chemical Education, 1988
Investigates minimizing errors in computational methods commonly used in chemistry. Provides a series of examples illustrating the propagation of errors, finite difference methods, and nonlinear regression analysis. Includes illustrations to explain these concepts. (MVL)
Descriptors: Chemistry, College Science, Computation, Computer Uses in Education