Social Science Statistics Blog

March 7, 2011

Type I and Type II Errors

A well-known social scientist once confessed to me that, after decades of doing social research, he still couldn't remember the difference between Type I and Type II errors. Since I suspect that many others also share this problem, I thought I would share a mnemonic I learned from a statistics professor. Recall that a Type I error occurs when the null hypothesis is rejected when it is in fact true, while a Type II error occurs when a null hypothesis is not rejected when it is actually false. This distinction, of course, many people find difficult to remember.

So here's the mnemonic: first, a Type I error can be viewed as a "false alarm" while a Type II error as a "missed detection"; second, note that the phrase "false alarm" has fewer letters than "missed detection," and analogously the numeral 1 (for Type I error) is smaller than 2 (for Type I error). Since learning this mnemonic, I have not forgotten the difference between Type I and Type II errors!

Posted by Ethan Fosse at 8:29 PM | Comments (16)

March 2, 2011

R Graph Cookbook

I've been waiting for this kind of R book for awhile. Packt Publishing, which releases technical and information technology books, has just published The R Graph Cookbook. The premise is simple: there is a need for a book that clearly presents "recipes" of R graphs in one comprehensive volume. Indeed, many researchers switch to R (from Stata or SAS) in part because of the enormous flexibility and power of R in creating graphs.

This book is perhaps most useful for beginners, but even experienced R users should find the clarity of the presentation and discussion of advanced graphics informative. In particular, I found the presentation of how to create heatmaps and geographic maps useful. I'll certainly use these examples when teaching data visualization. Another enormous benefit of the book is that the author has released all the R code used to create the graphs. You can download the R code here.

I have two quibbles, however. First, while the use of color in the graphs is pretty, I would've liked more examples with black-and-white templates. Although many decades from now (when most research might conceivably be published exclusively online), color graphs will be the norm, currently most research is published in journals where colors are not used. Second, like nearly all books I've seen on graphics using statistical packages, the author doesn't present graphics for regression coefficients and cross-tabs. (For information on graphing these, I recommend the excellent article on using graphs instead of tables, published in Perspectives in Politics.) Nonetheless, these are minor issues, and most R users, regardless of skill level, should find this book very useful for teaching and reference.

Posted by Ethan Fosse at 6:41 PM | Comments (2)