Author Archives: Nathan Tintle

About Nathan Tintle

Nathan Tintle is an Associate Professor of Statistics at Dordt College, Iowa.

Archived webinar/e-conference sessions

Listed below are a series of webinar/e-conference style presentations given by faculty using, or considering the use of, SBI methods

Batting for power (using a simulation-based approach)
Allan Rossman and Beth Chance (Cal Poly San Luis Obispo)
October 27, 2015

Reflections on making the switch to a simulation-based inference curriculum
Panelists: Julie Clark (Hollins), Lacey Echols (Butler), Dave Klanderman (Trinity), Laura Schultz (Rowan); Moderator: Nathan Tintle
September 8, 2015

Teaching the statistical investigation process with randomization-based inference
Nathan Tintle (Dordt College) and Beth Chance (Cal Poly San Luis Obispo)
eCOTS 2014

Teaching Randomization-based Methods in an Introductory Statistics Course: The CATALST Curriculum
Bob delMas, University of Minnesota
eCOTS 2014

StatKey – Online Tools for Teaching Bootstrap Intervals and Randomization Tests
with Robin Lock, St. Lawrence University
August 27th, 2013

Using Simulation to Introduce Inference for Regression
with Josh Tabor, Canyon del Oro High School
May 28th, 2013

Introducing inference with bootstrapping and randomization
with Kari Lock Morgan, Duke University
eCOTS 2012

Using Simulation Methods to Introduce Inference
with Kari Lock Morgan, Duke University
December 13th, 2011

Bootstrapping and randomization: Seeing all the moving parts
with Chris Wild, University of Auckland, New Zealand
November 22nd, 2011

Create an Iron Chef in statistics classes?
Rebekah Isaak, Laura Le, Laura Ziegler, and the CATALST Team
June 14th, 2011

Golfballs In The Yard – Using Simulation To Teach Hypothesis Testing
Randall Pruim, Calvin College
January 25th, 2011

“Using baboon “mothering” behavior to teach Permutation tests”
with Thomas Moore, Grinnell College
Sept 14, 2010

“Pedagogical simulations with StatCrunch”
with Webster West, Texas A&M University
July 13, 2010

Concepts of Statistical Inference: A Randomization-Based Curriculum
Allan Rossman & Beth Chance, Cal Poly – San Luis Obispo; and John Holcomb, Cleveland State University
April 14th, 2009

Teaching Statistical Inference via Simulation using R
Daniel Kaplan, Macalester College
October 14th, 2008

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

Nathan Tintle – Dordt College

ntintle

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. 

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. 

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