Literature Index

In the early 1990's, the National Science Foundation funded many research projects for improving statistical education. Many of these stressed the need for classroom activities that illustrate important issues of designing experiments, generating quality data, fitting models, and performing statistical tests. Our paper describes such an activity on logistic regression that is useful in second applied statistics courses. The activity involves students attempting to toss a ball into a trash can from various distances. The outcome is whether or not students are successful in tossing the ball into the trash can. This activity and the adjoining homework assignments illustrate the binary nature of a response variable, fitting and interpreting simple and multiple logistic regression models, and the use of odds and odds ratio

What follows are some suggestions upon module developments designed to provide problem-solving experiences in developing a sequence of trigonal,numerical arrays, JE(n), which in turn give rise to sequences of discrete, fair probability spaces, P(m(JE(n))), with practical suggestions for open-ended model building. Hopefully these module developments will give some insight into teaching and learning some aspects of problem-solving, as well as naturally generating probability spaces which are discrete and fair. The intent is that the module will contribute to the student's overall ability to understand probability and statistics at a more mature level of sophistication. These ideas were initiated about 1983 by the author, have been field tested with two groups of fifth grade students successfully, and many teachers at the public school level and university level have benefited from workshops based upon these modules.

Statisticians and Statistics teachers often have to push back against the popular impression that Statistics teaches how to lie with data. Those who believe incorrectly that Statistics is solely a branch of Mathematics (and thus algorithmic), often see the use of judgment in Statistics as evidence that we do indeed manipulate our results.<br><br>In the push to teach formulas and definitions, we may fail to emphasize the important role played by judgment. We should teach our students that they are personally responsible for the judgments they make. But we must also offer guidance for their statistical judgments. Such guidance requires that we acknowledge the role of ethics in Statistics. The principle guiding these judgments should be the honest search for truth about the world, and the principle of seeking such truth should have a central place in Statistics courses.<br><br>The remark attributed to Disraeli would often apply<br>with justice and force: "There are three kinds of lies:<br>lies, damn lies, and statistics".<br>-Mark Twain<br><br>This may be my least favorite quotation about Statistics. But I wish to address what underlies both the quotation and the gleeful willingness of many who know nothing at all about Statistics to quote it as if it justified their low opinion of the discipline.<br><br>This quotation has infiltrated discussions in many disciplines. Surely you have had it quoted back to you if you were foolish enough to admit in polite company that you teach Statistics. Nigel Rees's Quote...Unquote1 claims that this is the single most quoted remark in the British media.2 A Google books search of "lies, damn lies, and statistics" turns up 495 books, and a general Google search finds "about 207,000" hits. A small (nonrandom) sample of these references shows that most are meant to suggest dishonest manipulations and interpretations.

Despite advances in computer technology, quantiles of Student's t (among other distributions) are still calculated using printed tables in most classroom situations. Unfortunately, the structure of the tables found in textbooks (and even in books of tables) is usually better suited to fixed-level hypothesis testing than to the p-value approach that many modern statisticians favor. This article presents a novel arrangement of the table that allows p-values to be determined quite precisely from a table of manageable size.