Monday, December 22, 2014

Justin Wolfers' "A Persuasive Chart Showing How Persuasive Charts Are"

Wolfers (writing for the New York Times) summarizes a study from Wansink and Tal (2014) in which participants were either a) presented with just in-text data about a drug trial or b) the text as well as with a bar graph that conveyed the exact same information. The results can be read below:


Wolfers/NYT

According to Wansink and Tal, the effects seems to be strongest in people who agreed with the statement "I believe in science". So, a graph makes a claim more "sciencier" and, therefore, more credible? Also, does this mean that science believers aren't being as critical because they already have an underlying belief in what they are reading? 

I thinks this is a good way of conveying the power of graphs to students in a statistics class as well as the need for better scientific literacy/statistical consumerism/skepticism. I think it is also a good example of Elaboration Likelihood Model

Finally, can we take just one moment to discuss the name of the original research article, Blinded with science: Trivial graphs and formula increase ad persuasiveness and belief in product efficacy. I'm always a fan of the "<funny cultural reference>: <serious science-y stuff>" naming convention oft used in scientific articles, and this is a real gem.


Monday, December 15, 2014

Kristopher Magnusson's "Interpreting Cohen's d effect size"

Kristopher Magnusson (previously featured on this blog for his interactive illustration of correlation) also has a helpful illustration of effect size. While this example probably has some information that goes beyond an introductory understanding of effect size (via Cohen's d) I think this still does a great job of illustrating how effect size measures, essentially, the magnitude of the difference between groups (not how improbably those differences are). See below for a screen shot of the tool.

http://rpsychologist.com/d3/cohend/, created by Kristopher Magnusson

Wednesday, December 10, 2014

UCLA's "What statistical analysis should I use?"

This resource from UCLA is, essentially, a decision making tree for determining what kind of statistical analysis is appropriate based upon your data (see below).
Screen shot from "What statistical analysis should I use?"
Now, such decision making trees are available in many statistics text book...however...
what makes this special is the fact that with each test comes code/syntax as well as output for SAS, Stata, SPSS, and R. Which is helpful to our students (and, let's be honest, us instructors/researchers as well).

Monday, December 1, 2014

Tessa Arias' "The Ultimate Guide to Chocolate Chip Cookies"

I think this very important cookie research is appropriate for the Christmas cookie baking season. I also believe that it provides a good example of the scientific method.

Arias started out with a baseline cookie recipe (baseline Nestle Toll House Cookie Recipe, which also served as her control group) and modified the recipe in a number of different ways (IVs) in order to study several dependent variables (texture, color, density, etc.). The picture below illustrates the various outcomes per different recipe modifications.

For science!

http://www.handletheheat.com/the-ultimate-guide-to-chocolate-chip-cookies


Also, being true scientist, her original study lead to several follow up studies investigating the effect of different kinds of pans and flours upon cookie outcomes.

http://www.handletheheat.com/the-ultimate-guide-to-chocolate-chip-cookies-part-2
I used this example to introduce hypothesis testing to my students. I had them identify the null and alternative hypotheses, the control and experimental groups, etc.