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 ...

Recently, a Facebook craze asked users to list three things you are thankful for for five days. Data scientis ts Winter Mason, Funda Kivran-Swaine,  Moira Burke, and Lada Adamic  at Fa cebook have analyzed this dat a to better understand the patterns of gratitude publically shared by Facebook users. The data analysts broke down data by most frequently listed gratitude topic: Most frequently "liked" gratitude posts: (lots of support for our friends in recovery, which is nice to see). Gender differences in gratitude...here is data for women. The wine gratitude finding for women was not present in the data for men. Ha. Idiosyncratic data by state. I would say that Pennsylvania's fondness for country music rings true for me. How to use in class: This example provides several interesting, easy to read graphs, and the graphs show how researchers can break down a single data set in a variety of interesting ways (by gender, by age, by state). Add...

This Scientific American podcast by Diane Fine Maron describes how the Chicago Department of Public Health (CDPH) used Twitter data to shut down restaurants with health code violations. Essentially, the CDPH monitored Tweets in Chicago, searching for the words "food poisoning". When such a tweet was identified, an official at CDPH messaged the Twitterer in question with a link to an official complain form website. The results of this program? "During a 10-month stretch last year, staff members at the health agency responded to 270 tweets about “food poisoning.” Based on those tweets, 193 complaints were filed and 133 restaurants in the city were inspected. Twenty-one were closed down and another 33 were forced to fix health violations. That’s according to a study in the journal  Morbidity and Mortality Weekly Report.  [Jenine K. Harris et al,  Health Department Use of Social Media to Identify Foodborne Illness — Chicago, Illinois, 2013–2014 ]" I think this is ...