# Category Archives: Statistics

There are plentiful examples of spreadsheet applications leading analysts astray. Believe all the scary stories. Spreadsheets can silently damage your data, converting numbers to dates or dropping leading zeros from what should be fixed-length identifier (where did the U.S. Zip Code 01002 go?).

The U.K. Lost 16,000 COVID Cases Because It Doesn’t Understand Microsoft Excel

The Reinhart-Rogoff error or how not to Excel at economics

For a set of best practices when working with data in spreadsheets, take to heart the advice offered by Data Organization in Spreadsheets. Please!

# Compact Guide to Classical Inference by Daniel Kaplan (or: How to Teach Stats)

“Both students and instructors perceive standard-error statistics as a confusing collection of specialized tools. To improve student learning, instructors long for a reduction in the number of topics needed to support statistical thinking. This book is a roadmap for instructors who wish to simplify inference while continuing to teach using traditional tools.”

“I hope that this little book can help instructors see that statistical inference can be handled as one topic among the many needed for modern statistics. Inference, important though it be, does not need to be such a sprawling set of methods and details taking up so much of the introductory course that other essential topics get neglected.”

https://dtkaplan.github.io/CompactInference

# Common statistical tests are linear models (or: how to teach stats)

This post by Jonas Kristoffer Lindell presents a parsimonious view of common statistical tests which are, on their own, confusingly and inconsistently named and, taken together, a mess.

Jonas argues that there is a common theme among these tests and that it is simple. Simple to explain and simple to understand.

I highly recommend you give it a read.

https://lindeloev.github.io/tests-as-linear/

# Dependent Sample Assessment Plots Using granovaGG and R

9/4/2011 Update: granovaGG is now available directly from CRAN.

Just over one year ago, I wrote about creating Dependent Sample Assessment Plots (DSAP) Using granova and R. Since then, Brian Danielak has been developing a new, ggplot2-based version of granova named granovaGG, which is almost ready for release on CRAN. This article updates my earlier granova-based version, but leaves much of the article text unchanged.
Continue reading Dependent Sample Assessment Plots Using granovaGG and R

# Dependent Sample Assessment Plots Using granova and R

Dependent Sample Assessment Plots (DSAP) constitute a way of visualizing data in the context of two dependent sample analyses. One (of at least four ways[1. See Pruzek and Helmreich’s paper in the Journal of Statistics Education Volume 17, Number 1 (2009), Enhancing Dependent Sample Analyses Using Graphics]) to think about this would be to think of pre-intervention and post-intervention response data scores, when studying the effects of intervention.

Suppose you’re an educator and you administer an assessment to students at the beginning of a unit asking about their level of confidence or understanding of a topic. You then teach a lesson that spans some period of time. At the end you collect responses to the same questions again. You now have a dependent sample: two responses that related to the same individual for some number of individuals.