Here is a great example of several different topics, featuring an engaging, interactive m ap created by Time magazine AND using data from a Journal of Personality and Social Psycholog y article . Essentially, the authors of the original article gave the Big Five personality scale to folks all over the US. They broke down the results by state. Then Time created an interactive map of the US in order to display the data. http://time.com/7612/americas-mood-map-an-interactive-guide-to-the-united-states-of-attitude/ How to use in class:

Data USA draws upon various federal data sources in order to generate visualizations about cities and occupations in the US. And it provides lots of good examples of simple, descriptive statistics and data visualizations. This website is highly interactive and you can query information about any municipality in the US. This creates relevant, customized examples for your class. You can present examples of descriptive statistics using the town or city in which your college/university/high school is located or you could encourage students to look up their own hometowns. Data provided includes job trends, crime, health care, commuting times, car ownership rates...in short, all sorts of data. Below I have included some screen shots for data about Erie, PA, home of Gannon University: The background photo here is from the Presque Isle, a very popular state park in Erie, PA. And, look, medians!

UPDATE, 9/22/22: Here is a non-paywalled link to this information:  https://www.nytimes.com/2017/10/09/learning/whats-going-on-in-this-graph-oct-10-2017.html This article from the NYT is based on a survey . That survey asked a bunch of nutritionists if they considered certain foods healthy. Then they asked a bunch of everyday folks if they considered the same foods to be healthy. Then they generated the percentage of each group that considered the food healthy. And the NYT put the nutritionist responses on a Y-axis, and commoners on the X, and made a lovely scatterplot... Nutritionists and non-nutritionists agree that chocolate chip cookies are not healthy. However, nutritionists are far more critical of American cheese than are non-nutritionists.  ...and provided us with the raw data as well.

Good data should inform our decisions. Even a really stressful decision. This site demonstrates this beautifully by providing UK pediatric hospital survival rates to aid the parents of children undergoing heart surgery. The information is translated for laypeople. They present statistical ideas that you and your students have heard of but without a lot of statistical jargon. The data is also explained very clearly. For example, they  present detailed hospital survival rates , which include survival ranges: So, it contains data from a given period. It includes the actual mortality rate and a range likely to have a valid mortality rate. So, essentially, they are confidence intervals but not precisely confidence intervals. In addition to this more traditional presentation of the data, the survival ranges are explained in greater detail in a video . I think this video is helpful because it describes the distribution of the sample mean and how to use them to estimate ac...

UPDATE: 9/22: Sex ratio in India is normalizing: https://www.pewresearch.org/religion/2022/08/23/indias-sex-ratio-at-birth-begins-to-normalize/ I use this story from The Economist as a conceptual explanation of the one-sample t-test.  TL:DR: Sex ratio disparity data out of India is an abstract introduction to the one-sample t -test. So, at its most basic, one sample t -test uses some given, presumably true number/mu and tests your sample against that number. This conceptual example illustrates this via the naturally occurring sex ratio in humans (your mu) versus 2006-8 sex ratio data from different states in India (your sample data). Why look at this data? Social pressure, like dowries, high rates of sexual violence against women in India, etc., make male offspring more attractive than female offspring to some families. And the data provides evidence that this is leading to disturbing demographic shifts. For example, see the table below from The Economist: http://www.ec...

Organizations have frequently tried to shut down/manipulate data for their own ends. Big tobacco and lung cancer and addiction research . The National Football League and Chronic Traumatic Encephaly . And for the last 20 years, the National Rifle Association has successfully blocked funding for research investigating public safety and gun ownership. Essentially, the NRA has concentrated on eliminating funding at the CDC for research related to a better understanding of how guns hurt people. It started in 1996 with the Dickey Amendment and no one has been willing to fight to bring back funding. The APA wrote a piece on this in 2013 that summarizes the issue. In the wake of the shooting in Orlando, NPR did a story explaining how the American Medical Association is trying to change the rules governing gun research  and  the L.A. times published this column . I think this precedence is unfortunate from both sides of the gun debate. I grew up in rural Pennsylvania. I've...

I love research that is counterintuitive. It is interesting to me and makes a strong, memorable example for the classroom. That's why I'm recommending Carroll's piece  from the NYT. It questions the conventional wisdom that breakfast is the most important meal of the day. As Carroll details, there is a long standing and strong belief in nutrition research claiming that breakfast reduces obesity and leads to numerous healthy outcomes. But most nutrition research is correlational, not causal. AND there seems to be an echo-chamber effect, such that folks are miss-citing previous nutrition research to bring it in line with the breakfast research. Reasons to use this article as a discussion piece in your statistics or research methods course: -Highlights the difference between correlation and causation -Provides an easy to understand example of publication bias ("no breakfast = obesity" is considered a fact, studies that found the opposite were less likely to...

If you are familiar with financial and racial disparities that exist in the US, you can probably guess where this article is going based on its title. Kids in wealthy school districts do better in school than poor kids. Within each school district, white kids do better than African American and Latino kids. How did they get to this conclusion? For every school district in the US, the researchers used the Stanford Educational Data Archive to figure out 1) the median household income within each school district and 2) the grade level at which the students in each school district perform (based on federal test performance). This piece also provides multiple examples for use within the statistics classroom. Highly sensitive examples, but good examples none the less. -Most obviously, this data provides an easy-to-follow example of linear relationships and correlations. The SES:school performance relationship is fairly intuitive and easy to follow (see below) From the New Yor...

MOOCs aren't just for current students. I think they can serve as professional development for faculty members as well. I don't have time for a MOOC during the school year, but I am committing to doing one this summer. I think that instructors can approach MOOCs in two ways: 1) professional development, and 2) a search for improved pedagogy. As professional development, learn a new statistical skill or freshen up a dormant one. Learn R. Learn Python. Freshen up on your non-parametric skillz. Take a course on data mining or using statistics in order to gain business insights. Unofficial documentation of your course progress is typically offered just by taking the course. Official documentation/grade reports are usually available for a reasonable fee (my husband has taken a few such philosophy courses and paid around $50 for the official documentation). Another way to use these courses: Don't take them to learn new skills, take them to learn new ways to teach your old...

Hackathorn, Ashdown, & Rife just shared some great resources for using humor to teach statistics.  In their own words, " This resource consists of a 21-page word document that reviews literature on the use of humor in teaching, describes an instrument for assessing the use of classroom humor, and offers tips on using two additional resource features specific to teaching statistics: (a) 42 visual jokes and cartoons, organized by 12 statistical topics, and (b) 12 slide presentations." You guys. It is a collection of hilarious jokes and memes to use when teaching. As well as some scholarly work about using humor to teach.  Here is a link that will download the .zip file to your computer. Here is a link to STP's Office of Teaching Resources in Psychology's Teaching Resources. Scroll on down to the Statistics, Research, and Teaching header to find this resource. A few samples of the cartoons they included:

This hilarious video is making the rounds on the Interwebz. Kudos to John Oliver and his writing team for so succinctly and hilariously summarizing many different research problems...why replication is important but not rewarded, how research is presented to the public, how researchers over-reach about their own findings, etc.  I Tweeted about this, but am making it cannon by sharing as a blog post. Note: This video has some off-color humor (multiple references to bear fellatio) so it is best suited to college aged students. I will use this in my Online and Honors classes as discussion prompts. Here are some of the prompts I came up with: 1) In your own words, why aren't replications published? How do you think the scientific community could correct this problem?  2) In your own words, explain just ONE of how a RESEARCHER can manipulate their own data and/or research findings. It should be one of the methods of manipulation described in the video. Also, don't just na...

People don't always tell the whole truth on dating websites, embellishing the truth to make themselves more desirable. This example of how OK Cupid users lie about their heights is a good example for conceptually explaining null hypothesis testing, t -tests, and normal distributions. So, Cheng, article author and data enthusiast, looked through OK Cupid data. In this article, she describes a few different findings, but I'm going to focus on just one of them: She looked at users' reported heights. And she found a funny trend. Both men and women seem to report that they are taller than they actually are. How do we know this? Well, the CDC collects information on human heights so we have a pretty good idea of what average heights are for men and women in the US. And then the author compared the normal curve representing human height to the reported height data from OK Cupid Users. See below... From http://nycdatascience.com/okcupid-scraper/, by Fangzhou Cheng  ...

I hope you are all finishing your grading and enjoying the first bit of your summer vacations (those of you who teach at colleges that run on semesters, at least!). I know that the last thing you want to think about right now is professional development. Especially professional development related to the teaching of statistics. That being said, I wanted to remind everyone that the Electronic Conference on the Teaching of Statistics (eCOTS) is happening next week (May 16-20) . As a mom and generally exhausted person, I really appreciate the chance to attend a conference in my pajamas. Here is the program . It only costs $25, includes workshops and poster sessions, and recordings from the conference. So, even if you aren't mentally ready to tackle something like this right now, you could view the materials at a later date.