The original idea for using this article this way comes from Dr. Susan Nolan 's presentation at NITOP 2015, entitled " Thinking Like a Scientist: Critical Thinking in Introductory Psychology."  I think that Dr. Nolan's idea is worth sharing, and I'll reflect a bit on how I've used this resource in the classroom. (For more good ideas from Dr. Nolan, check out her books, Psychology , Statistics for the Behavioral Sciences , and The Horse that Won't Go Away (about critical thinking)). Last summer, the National Academy of Sciences Proceedings published an article entitled "Experimental evidence of massive-scale emotional contagion through social networks ." The gist: Facebook manipulated participants' Newsfeeds to increase the number of positive or negative status updates that each participant viewed. The researchers subsequently measured the number of positive and negative words that the participants used in their own status updates. They fou...

Jethro Waters, Dan Peterson, Ph.D., Laurie McCollough, and Luke Norton made a pair of animated videos ( 1 , 2 ) that explain why correlation does not equal causation and how we can perform lab research in order to determine if causal relationships exist. I like them a bunch. Specific points worth liking: -Illustrations of scatter plots for significant and non-significant relationships. Data does not support the old wive's tale that everyone goes a little crazy during full moons. -Explains the Third Variable problem. Simple, pretty illustration of the perennial correlation example of ice cream sales (X):death by drowning (Y) relationship, and the third variable, hot weather (Z) that drives the relationship. -In addition to discussing correlation =/= causation, the video makes suggestions for studying a correlational relationship via more rigorous research methods (here violent video games:violent behavior). Video games (X) influence aggression (Y) via the moderato...

Back in the day, I remember having to complete an online research ethics course in order to serve as an undergraduate research assistant at Penn State. I think that such training could be used as an exercise/assessment in a research methods class or an advanced statistics class. NOTE: These examples are sponsored by the American agencies and, thus, teach participants about American laws and rules. If you have information about similar training in other countries (or other free options for American researchers), please email me and I will add the link. Online Research Ethics Course from the U.S. Health and Human Service's Office of Research Integrity. Features: Six different learning modules, each with a quiz and certificate of completion. These sections include separate quizzes on the treatment of human and animal test subjects. Other portions also address ethical relationships between PIs and RAs and broader issues of professional responsibility when reporting results. ...

It's coming, guys. But let's get ahead of it. I thought I would re-share some resources that you may want to consider working into your curriculum this year. I picked out a few lessons and ideas that also require a bit of forethought and planning, especially if they become assessment measures for your class. Center for Open Science workshops: As previously discussed on this blog , COS offers f ree consultation  (face-to-face or online) to faculty and students in order to teach us about the open framework for science. They provide guidance about more more traditional statistical issues, like power calculations and conducting meta-analysis in addition to lessons tailored to introducing researchers to the COS framework. Take your students to an athletic event , talk about statistics and sports : I took my students to a baseball game and worked some statsy magic. You can do it, too. If not a trip to the ballpark, an on-campus or televised athletic event will w...

Ben Blatt of Slate mined through Center for Disease control data in order to provide us with 13 different maps of the United States and mortality information for each state . Below, information on disproportionately high cases of death in each state. While the maps are morbid and interesting, the story behind the maps ( read the story here about how data can be easily misrepresented by maps ) make this a good example of how easily data can be distorted. The story along with the maps unveils several issues that statisticians/researchers must consider when they are presenting descriptive statistics. In this instance, Blatt had to sort through the data to eliminate the most common causes of death (heart disease, cancer, etc.) in order to uncover unique data for each state. Relatedly, he highlights the fact that "disproportionately" does not mean "most": "But this map—like many maps which purport to show attributes meant to be “distinct” or “disproporti...