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CadwalladerOlsker, Todd – Mathematics Teacher, 2019
Students studying statistics often misunderstand what statistics represent. Some of the most well-known misunderstandings of statistics revolve around null hypothesis significance testing. One pervasive misunderstanding is that the calculated p-value represents the probability that the null hypothesis is true, and that if p < 0.05, there is…
Descriptors: Statistics, Mathematics Education, Misconceptions, Hypothesis Testing
Rossman, Allan; Utts, Jessica – Journal of Statistics Education, 2014
This article offers a transcript of author Allan Rossman's interview with Jessica Utts, Professor and Chair of Statistics at the University of California-Irvine. Utts is also a Fellow of the American Statistical Association and a recipient of a Founders Award from ASA. Additionally, she has been elected as President of ASA for the year 2016. The…
Descriptors: Interviews, Statistics, College Faculty, College Mathematics
Balasooriya, Uditha; Li, Jackie; Low, Chan Kee – Australian Senior Mathematics Journal, 2012
For any density function (or probability function), there always corresponds a "cumulative distribution function" (cdf). It is a well-known mathematical fact that the cdf is more general than the density function, in the sense that for a given distribution the former may exist without the existence of the latter. Nevertheless, while the…
Descriptors: Computation, Probability, Mathematics, Mathematics Curriculum
Drummond, Gordon B.; Tom, Brian D. M. – Advances in Physiology Education, 2011
Statisticians use words deliberately and specifically, but not necessarily in the way they are used colloquially. For example, in general parlance "statistics" can mean numerical information, usually data. In contrast, one large statistics textbook defines the term "statistic" to denote "a characteristic of a…
Descriptors: Intervals, Research Methodology, Testing, Statistics
Petocz, Peter; Sowey, Eric – Teaching Statistics: An International Journal for Teachers, 2008
In this article, the authors focus on hypothesis testing--that peculiarly statistical way of deciding things. Statistical methods for testing hypotheses were developed in the 1920s and 1930s by some of the most famous statisticians, in particular Ronald Fisher, Jerzy Neyman and Egon Pearson, who laid the foundations of almost all modern methods of…
Descriptors: Hypothesis Testing, Statistical Inference, Statistics, Statistical Analysis
Satake, Eiki; Amato, Philip P. – AMATYC Review, 2008
This paper presents an alternative version of formulas of conditional probabilities and Bayes' rule that demonstrate how the truth table of elementary mathematical logic applies to the derivations of the conditional probabilities of various complex, compound statements. This new approach is used to calculate the prior and posterior probabilities…
Descriptors: Mathematical Logic, Probability, Mathematics Instruction, Statistics
Kern, John C. – Journal of Statistics Education, 2006
Bayesian inference on multinomial probabilities is conducted based on data collected from the game Pass the Pigs[R]. Prior information on these probabilities is readily available from the instruction manual, and is easily incorporated in a Dirichlet prior. Posterior analysis of the scoring probabilities quantifies the discrepancy between empirical…
Descriptors: Bayesian Statistics, Probability, Inferences, Statistics
Khuri, Andre – International Journal of Mathematical Education in Science and Technology, 2004
The Dirac delta function has been used successfully in mathematical physics for many years. The purpose of this article is to bring attention to several useful applications of this function in mathematical statistics. Some of these applications include a unified representation of the distribution of a function (or functions) of one or several…
Descriptors: Maximum Likelihood Statistics, Bayesian Statistics, Statistics, College Mathematics
Zhu, Mu; Lu, Arthur Y. – Journal of Statistics Education, 2004
In Bayesian statistics, the choice of the prior distribution is often controversial. Different rules for selecting priors have been suggested in the literature, which, sometimes, produce priors that are difficult for the students to understand intuitively. In this article, we use a simple heuristic to illustrate to the students the rather…
Descriptors: Bayesian Statistics, Maximum Likelihood Statistics, Probability, Statistical Distributions
Peer reviewedJarrell, Stephen – Mathematics and Computer Education, 1990
Explains a new way of viewing Bayes' formula. Discusses the revision factor and its interpretation. (YP)
Descriptors: Bayesian Statistics, College Mathematics, Computation, Decimal Fractions

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