Journal Article

  • Is it reasonable to teach the ideas and methods of Bayesian inference in a first statistics course for general students? This paper argues that it is, at best, premature to do so. Surveys of the statistical methods actually in use suggest that Bayesian techniques are little used. Moreover, Bayesians have not yet agreed on standard approaches to standard problem settings. Bayesian reasoning requires a grasp of conditional probability, a concept confusing to beginners. Finally, an emphasis on Bayesian inference might well impede the trend toward experience with real data and a better balance among data analysis, data production, and inference in first statistics courses

  • Despite widespread use of significance testing in empirical research, its interpretation and researchers' excessive confidence in its results have been criticized for years. In this article, the logic of statistical testing in the Fisher and Neyman-Pearson approaches are described, some common misinterpretations of basic concepts behind statistical tests are reviewed, and the philosophical and psychological issues that can contribute to these misinterpretations are analyzed. Some frequent criticisms against statistical tests are revisited, with the conclusion that most of them refer not to the tests themselves but to the misuse of tests on the part of researchers. In accordance with Levin (1998a), statistical tests should be transformed into a more intelligent process that helps researchers in their work. Possible ways in which statistical education might contribute to the better understanding and application of statistical inference are suggested.

  • The use of statistics and probabilities as legal evidence has recently come under increased scrutiny. Judges' and jurors' ability to understand and use this type of evidence has been of special concern. Finkelstein and Fairley (1970) proposed introducing Bayes' theorem into the courtroom to aid the fact-finder evaluate this type of evidence. The present study addressed individuals' ability to use statistical information as well as their ability to understand and use an expert's Bayesian explanation of that evidence. One hundred and eighty continuing education students were presented with a transcript purportedly taken from an actual trial and were asked to make several subjective probabiliy judgments regarding blood-grouping evidence. The results extend to the trial process previous psychological research suggesting that individuals generally underutilize statistical information, as compared to a Bayesian model. In addition, subjects in this study generally ignored the expert's Bayesian explanation of the statistical evidence.

  • The current context of the "significance test controversy" is first briefly discussed. Then experimental studies about the use of null hypothesis significance tests by scientific researchers and applied statisticians are presented. The misuses of these tests are reconsidered as judgmental adjustments revealing researchers' requirements towards statistical inference. Lastly alternative methods are considered. Consequently we automatically ask ourselves "won't the Bayesian choice be unavoidable?"

  • Confirmation bias, as the term is typically used in the psychological literature, connotes<br>the seeking or interpreting of evidence in ways that are partial to existing beliefs,<br>expectations, or a hypothesis in hand. The author reviews evidence of such a bias in a<br>variety of guises and gives examples of its operation in several practical contexts.<br>Possible explanations are considered, and the question of its utility or disutility is<br>discussed.

  • Definitive comprehensive overview of modalities that can be use to making learning statistics fun, including humor, song, books, games, game shows, literary works, word games, and celebrations. Most of the strategies are research-based and/or classroom tested and the paper includes a lengthy annotated bibliography.

  • The objective of the present paper is to provide a simple approach to statistical inference<br>using the method of transformations of variables. We demonstrate performance of this powerful<br>tool on examples of constructions of various estimation procedures, hypothesis testing, Bayes<br>analysis and statistical inference for the stress-strength systems. We argue that the tool of<br>transformations not only should be used more widely in statistical research but should become<br>a routine part of calculus-based courses of statistics. Finally, we provide sample problems for<br>such a course as well as possible undergraduate reserach projects which utilize transformations<br>of variables.

  • Students often enter an introductory statistics class with less than positive attitudes about the subject.<br>They tend to believe statistics is difficult and irrelevant to their lives. Observational evidence from<br>previous studies suggests including projects in a statistics course may enhance students' attitudes toward<br>statistics. This study examines the relationship between inclusion of a student-designed data collection<br>project in an introductory statistics course and 6 components comprising students' attitudes toward<br>statistics. The sample consisted of 42 college students enrolled in an introductory statistics course.<br>Comparisons of those who completed the student-designed data collection project (n = 24) and those who<br>did not complete the project (n = 18) suggest that inclusion of a project may not significantly impact<br>students' attitudes toward statistics. However, these findings must be viewed as only a preliminary step in<br>the study of the effect of projects on attitudes toward statistics.

  • The field of Data Mining like Statistics concerns itself with "learning from data" or "turning data into<br>information". For statisticians the term "Data mining" has a pejorative meaning. Instead of finding<br>useful patterns in large volumes of data as in the case of Statistics, data mining has the connotation of<br>searching for data to fit preconceived ideas. Here we try to discuss the similarities and differences as<br>well as the relationships between statisticians and data miners. This article is intended to bridge some of<br>the gap between the people of these two communities.

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