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# Conference Paper

• ### Activities to Promote Understanding of Variability

Variability is a complex concept with many facets.<br>Students build up their understanding of variability over time.<br>Variability is used for descriptive purposes, but is also an essential concept for understanding inference (sampling distributions, confidence intervals, p-values, etc.)<br>Therefore, helping students develop an understanding of variability (how it is measured, what it represents, how it is represented, how to use it to make comparisons) may require revisiting the concept throughout the course.

• ### The Craft of Teaching

Remarks on receiving the MAA's 1994 Award for Distinguished College or University Teaching of Mathematics, San Francisco, California, January 1995. MAA Focus 15 (1995) Number 2, 5-8

• ### Engaging the Intuition in Statistics to Motivate

An overview of how to motivate and bring intuition to concepts that are initially nonintuitive or even counterintuitive to students. Examples are provided that use a variety of means, including using multiple representations, intuitive analogies, and using (and resolving) counterintuitive examples. A thorough bibliography of additional resources and references is included.

• ### Factors influencing the psychology student in dealing with statistics courses.

Psychology students usually rate Statistics courses among the most difficult. The objective of the present study is to explore some aspects of the difficulties encountered by Psychology students in studying statistics and how these difficulties relate to statistics anxiety. A questionnaire measuring Psychology students' evaluation of the level of difficulty of the statistics courses studied; together with their opinions concerning the reasons for these difficulties was administered to a sample of 152 female undergraduate Psychology students at Cairo University. In addition, a measure of statistics anxiety was also used. Difficulties reported by students were in five categories in the following order: course content, teaching, examinations, relevance of statistics, and student characteristics. The perceived level of difficulty and abstraction were related to attitudes and opinions towards statistics and to the grades of the previous year.

• ### Evaluating the socio-economic relationship with the absence of reading between the students of a private and of a public school.

This work has as objective to verify if the socioeconomic relation of the students of two schools of the metropolitan area of Bel&eacute;m, one of a public school and another of a private school, interferes in the habit of reading of the same ones. To obtain those results the techniques statistical Analysis of Correspondence and Faces of Chernoff were used to present those students' profile.

• ### Meta-analysis: Pictures that explain how experimental findings can be integrated.

Meta-analysis (MA) is the quantitative integration of empirical studies that address the same or similar issues. It provides overall estimates of effect size, and can thus guide practical application of research findings. It can also identify moderating variables, and thus contribute to theory-building and research planning. It overcomes many of the disadvantages of null hypothesis significance testing. MA is a highly valuable way to review and summarise a research literature, and is now widely used in medicine and the social sciences. It is scarcely mentioned, however, in introductory statistics textbooks. I argue that MA should appear in the introductory statistics course, and I explain how software that provides diagrams based on confidence intervals can make many of the key concepts of MA readily accessible to beginning students.

• ### How the noncentral t distribution got its hump.

Once upon a time there was only one t distribution, the familiar central t, and it was monopolised by the Null Hypotheses (the Nulls), the high priests in Significance Land. The Alternative Hypotheses (the Alts) felt unjustly neglected, so they developed the noncentral t distribution to break the monopoly, and provide useful functions for researchers-calculation of statistical power, and confidence intervals on the standardised effect size Cohen's d. I present pictures from interactive software to explain how the noncentral t distribution arises in simple sampling, and how and why it differs from familiar, central t. Noncentral t deserves to be more widely appreciated, and such pictures and software should help make it more accessible to teachers and students.

• ### Generating different data sets for linear regression models with the same estimates.

For teaching purposes it is sometimes useful to be able to provide the students in a class with different sets of regression data which, nevertheless give exactly the same estimated regression functions. In this paper we describe a method showing how this can be done, with a simple example. We also note that the method can be generalized for situations where the regression errors are not independently distributed with a constant covariance matrix.

• ### Questions to assess the understanding of statistical concepts.

The GAISE College Report recommends that introductory applied statistics courses should place greater stress on statistical concepts and less stress on definitions, computations, and procedures. The report also urges instructors to align assessments with learning goals. In this paper the authors explain how instructors can implement these two recommendations. They first review the extent to which questions directly related to concepts are found in popular texts and on websites created by the publishers of these texts. Following this review they provide examples of such questions in a variety of formats (multiple choice, fill-in-the blank, open-ended, etc.). The examples will be classified by the approximate level of educational objective contained in Bloom's Taxonomy. Finally, the paper will discuss the advantages and disadvantages associated with having students answer such questions electronically.

• ### How should we teach the use of probabilities to take decisions when testing statistical hypothesis?

Nowadays we can not ignore the use of computers in statistics calculations and the main reason for its use is that computations become faster and trustworthy. Almost all statistical software computes p-values so students and researchers can take their decisions only based on its "usual" value. If the p-value is lower than 0.05 then the null hypothesis for a statistical test is "simply" rejected. Do statistical tests users ask about the meaning of this software output? If we are testing statistical hypothesis we have the null hypothesis tested against an alternative hypothesis. Do statistical tests users think about them? Since the decisions are based on sampling, the statistical tests decisions involve uncertainty and so two types of errors can be made. Do statistical tests users think about them? A questionnaire was constructed and administered to students and researchers in order to make a first approach about those subjects.