Finding the Right Balance

Erin BlankenshipErin Blankenship, University of Nebraska-Lincoln

For me, the hardest part about getting started was finding the right balance in my classroom – the right balance between lecture and activities; the right balance between in-class and out-of-class learning; the right balance between student accountability and student responsibility. None of this, however, really had much at all to do with the randomization-based curriculum. I had taught courses for pre-service and in-service K-12 teachers that focused on simulation-based methods . . . I knew it was effective pedagogically. The hard part came when a colleague and I decided that we would try to flip our classrooms the same semester we implemented the randomization-based curriculum. And, that too in a classroom with 2-3 times as many students as a “typical” intro class in our department. [pullquote]… the right balance between lecture and activities; the right balance between in-class and out-of-class learning; the right balance between student accountability and student responsibility.[/pullquote]

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Moving from a traditional curriculum to a simulation/randomization-based curriculum

mccgaugheyMy move from the traditional curriculum to the simulation/randomization-based curriculum was confounded with the simultaneous move of inference to the beginning of the course. Not only was I going to dive in to simulation/randomization as the primary mode by which to develop student understanding of statistical significance, but I was going to try it while completely turning the traditional ordering on its head.[pullquote] But moving inference to week 2 means my students immediately experience statistics as a science, and they get this experience repeatedly throughout the course, and in the end showing them what statisticians do and how statisticians think is more important than my struggle with where to put the definitions of parameter and statistic.[/pullquote] Continue reading

Using Simulation-Based Inference in AP Statistics

Josh TaborJosh Tabor, Canyon del Oro High School

The AP Statistics course is designed to mimic a traditional college-level introductory statistics class. Students are expected to use z-tests for proportions, t-tests for means and slopes, and chi-square tests for distributions of categorical data. There are at least three good reasons to incorporate simulation-based inference methods in the AP course, however.[pullquote]Doing these simulations takes time up-front, but helping students understand the logic of inference through simulation saves time in the long-run.[/pullquote]

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Highlighting real statistical studies

KRossKevin Ross – Cal Poly, San Luis Obispo

One of the recommendations of the GAISE report is to “use real data where possible.” While this is great advice, perhaps an even better recommendation is to “always use real statistical studies.” This post describes some ways I highlight real studies in my courses. While my approach might not be novel, I hope you find some of these ideas useful. [pullquote]Highlighting real data in our teaching is extremely important.   However, perhaps a better goal is to highlight real statistical studies…[/pullquote]

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Emphasizing the entire research process throughout the curriculum: The next step in real data integration in introductory statistics courses

Nathan Tintle – Dordt College

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In 2005, the Guidelines for Assessment and Instruction in Statistics Education made six recommendations about how we should teach introductory statistics. One of these recommendations is to use real data. The report goes on to argue that real data, as opposed to merely realistic (made-up for a hypothetical context) or naked (no context provided) data, is preferred. I argue that we should go a step further by emphasizing the entire statistical research process throughout the curriculum. [pullquote]To ensure our students leave our courses recognizing the indispensable nature of statistics in science and society we must force ourselves to get out of the box and embrace teaching the entire research context by utilizing real data that matters. [/pullquote] Continue reading

Should we teach the bootstrap or not in introductory statistics courses?

Chris Malone – Winona State University

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One’s success in a course is often determined by his or her desire and motivation to learn.  Unfortunately, desire and motivation are often lacking in an introductory statistics course.  I have learned some tricks over my years of teaching to enhance motivation — leverage their existing knowledge whenever possible and require students to repeatedly consider the phrase “What would happen if … .”[pullquote]Modern technologies and the recent advances in the use of simulation-based methods in teaching introductory statistics have allowed students to easily consider a variety of “What would happen if …”  scenarios. [/pullquote] Continue reading

Facilitating AP Statistics student projects

Robert Lochel, Hatboro-Horsham High School

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The Advanced Placement Statistics curriculum contains many natural opportunities for students to demonstrate their understanding through projects. In my course, students complete three major projects during the year: an “old wives’ tale” experimental design project, a casino game design project, and a final comprehensive project after the AP exam in May.  Balancing my desire to have students think critically and creatively about a research question, while providing some structure to help students reach clear assessment targets, isn’t always easy. Here are some suggestions for helping teachers design project-based learning experiences.[pullquote] … this was a “what if” we could have avoided by clearly defining each stage of the project before collecting data. [/pullquote] Continue reading

Are introductory statistics students ready to be laced up for the bootstrap?

Karsten Maurer – Iowa State University

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In this post, I provide my opinion on whether or not we should teach the bootstrap in introductory statistics courses.  I think this question is best answered in two parts: (1) can introductory students generally understand bootstrapping concepts and (2) is the additional bootstrapping material beneficial for student learning. The first component is effectively questioning “can we?” which is necessary before we try to answer the question, “should we?” My short answer to both of these is an emphatic, yes!  We can and should teach the bootstrap in introductory statistics courses.  My slightly longer answer follows in the remainder of this post.[pullquote]My short answer … is an emphatic, yes!  [/pullquote]

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Using student projects in a simulation-based inference curriculum…And vice versa

Dianna J. Spence – University of North Georgia

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Student-directed projects have been a staple of my introductory statistics course for several years, as I want students to learn statistical inquiry through authentic experience. By “student-directed” I mean that the student (or team of 2-3) crafts a research question, defines appropriate variables, collects data, and identifies and uses the correct statistical analysis to address their question. I don’t give the students a list of topics to choose from; I want them to come up with topics based on their interests, and to come up with all of the supporting details.[pullquote]Here’s how I have organized the course to use both SBI and projects, and how I modified the projects themselves to leverage the benefits that SBI brought to the course.[/pullquote] Continue reading

What teachers should know about the Bootstrap: Resampling in the undergraduate statistics curriculum

Tim Hesterberg – Google

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Here are some arguments for why we should not use bootstrap methods and permutation tests in teaching Stat 101:

  • Our usual cookbooks of formulas is such a resounding success, inspiring generations of students to further study (and rewarding their instructors with stellar reviews),

[pullquote]Bootstrapping and permutation tests make hard abstract concepts like sampling distributions, p-values, standard errors, and confidence intervals more concrete;[/pullquote] Continue reading