John Gabrosek, JSE Editor

The July issue of JSE includes 10 regular articles, two new Teaching Bits, and a rejoinder from Deb Rumsey to the letter from Michael Granaas in the March issue about whether to teach variance.

Two papers look at graphical representations. In “Normal Approximations to the Distributions of the Wilcoxon Statistics: Accurate to What N? Graphical Insights,” Carine A. Bellera, Marilyse Julien, and James A. Hanley use graphical insights to help students understand the logic of rank-based tests. Linda L. Cooper and Felice S. Shore discuss student misconceptions in interpreting variability from histograms, distribution bar graphs, and value bar charts in “The Effects of Data and Graph Type on Concepts and Visualizations of Variability.” They also introduce several graphical tools for visualizing variability.

Four papers discuss aspects of teaching statistical inference. In “Random Numbers Demonstrate the Frequency of Type I Errors: Three Spreadsheets for Class Instruction,” Sean Duffy provides three Excel worksheets for teachers and students to use when investigating type I error. The exercises are designed to address issues related to testing multiple relations using correlation, t-tests varying in sample size, and multiple comparisons using analysis of variance.

Steven D. LeMire discusses the framework for statistical hypothesis testing in the context of Toulmin’s model of argument in “An Argument Framework for the Application of Null Hypothesis Statistical Testing in Support of Research.” Josh Tabor uses simulation to delve deeper into the 2009 AP Statistics exam investigative task that asked students to construct a measure of skewness. In “Investigating the Investigative Task: Testing for Skewness. An Investigation of Different Test Statistics and their Power to Detect Skewness,” he compares the power of some of the most common student responses.

The fourth paper that deals with inference is by Aaron Weinberg, Emilie Wiesner, and Thomas J. Pfaff and titled “Using Informal Inferential Reasoning to Develop Formal Concepts: Analyzing an Activity.” The authors discuss a hands-on activity designed to help students draw informal inferences about a bag of bingo chips and connect these ideas to the formal t-test and confidence interval.

The remaining four regular papers cover a variety of topics. Felicity B. Enders, Sarah Jenkins, and Verna Hoverman discuss the use of peer review in teaching multiple regression to graduate students in “Calibrated Peer Review for Interpreting Linear Regression Parameters: Results from a Graduate Course.”

In “Maximizing a Probability: A Student Workshop on an Application of Continuous Distributions,” Martin Griffiths discusses an activity that can be used to introduce students to several common continuous probability models.

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In the second paper of a series, Jennifer Kaplan, Diane G. Fisher, and Neal T. Rogness look at five statistical terms and the meanings most commonly expressed by students at the end of an undergraduate statistics course in “Lexical Ambiguity in Statistics: How Students Use and Define the Words Association, Average, Confidence, Random, and Spread.”

Amanda S. Williams looks at the role instructor immediacy plays in student anxiety in “Statistics Anxiety and Instructor Immediacy.” Results show that instructor immediacy is related to six factors of statistics anxiety.

Audbjorg Bjornsdottir and Joan Garfield highlight recent papers of interest in “Teaching Bits: Statistics Education Articles from 2009 and 2010.” Michelle Everson and Ellen Gundlach focus on recent additions in “Teaching Bits: What’s New with CAUSEweb and MERLOT?”

Finally, JSE bids farewell to the regular Teaching Bits feature written by Deborah J. Rumsey. In her final contribution, Rumsey responds to Michael Granaas’ letter in the March issue about whether and when variance should be taught in “Teaching Bits: Random Thoughts on Teaching.”

The rejoinder is to “Let’s Just Eliminate the Variance.”