NEVER MIND ABOUT THIS ONE, GUYS! https://hal.sorbonne-universite.fr/hal-01580259/file/Dragicevic_Jansen_2017.pdf (Note the second author). ___________________________________________________________ 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 seem 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 think this is a good way of conveying the power of graphs to students in a statistics class as well ...

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

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).

As created by Jess Hartnett.

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 ...