This syllabus for  PSY 607: Everything is Fucked ,  made the rounds last week. The syllabus is for a course that  purports  that science is fucked. The course readings are a list of articles and books that hit on the limitations of statistics and research psychology ( p -values, shortcomings of meta-analysis, misuse of mediation, replication crisis, etc.). PSY 607 isn't an actual class ( as author/psychologist/blogger Srivastava explains in this piece from The Chronicle ) but it does provide a fine reading list for understanding some of the current debates and changes in statistics and psychology.  Most of articles are probably too advanced for undergraduates but perfectly appropriate for teaching graduat e students about our field and staying up to date as instructors of statistics. Here is a link to the original blog post/syllabus. 

This study, which found a link between maternal Tylenol usage during pregnancy and ADHD, has been making the rounds, particularly in the Academic Mama circles I move in. Being pregnant is hard. For just about every malady, the only solution is to stay hydrated. With a compromised bladder. But at least pregnant women have Tylenol for sore hips and bad backs. For a long time, this has been the only safe OTC pain reliever available to pregnant women. But a recent research article has cast doubt on this advice. A quick read of this article makes it sound like you are cursing your child with a lifetime of ADHD if you take Tylenol. A nd this article has become click-bait fodder. But these findings have some pretty big caveats.  Harris published this reaction piece at NPR . It is a good teaching example of media hype vs. incremental scientific progress and the third (or fourth or fifth) variable problem. It also touches on absolute vs. relative risk. NOTE: There are well-documente...

This example is a little out of my wheel house, but I'm putting it up here for those of you who teach more advanced UG stats or grad stats. I have never taught Principle Component Analysis. But Anh Le, PhD candidate at Duke, provides a detailed description of PCA in R AND does so using data that your advanced undergraduate/graduate students will enjoy: Pokemon.  So, Le downloaded data for each of the 151 Pokemon (individual stats for the strengths and weakness of each Pokemon, and provided a link so that you can download the data as well). He even included the code he used to create his PCA via R AND he does a nice job talking the reader through his process and what the findings mean. At 37, I didn't realize how much my traditionally-aged college students love Pokemon. Pokemon came up in my undergraduate I/O class three years ago, and I was shocked by how much nostalgia my then-20 year old students felt for the franchise. I think that it is certainly experiencing a rev...

Unsettling. But I bet your students won't forget this example of why mean isn't always the best measure of central tendency. While the reddit user labeled this as example median's superiority, you could also use this as an example when mode is useful. As statisticians, we often fall back on to mode when we have categories and median when we have outliers, but sometimes either median or mode can be useful when decimal points don't make a lot of sense. Here is the image and commentary from reddit: And this an IG posting about the data from the same user, Mona Chalabi from fivethirtyeight. I included the Instagram because Chalabi expands a bit more upon the original data she used. https://www.instagram.com/p/BIVKJrcgW51/

No, Wikipedia isn't a proper resource for our students to cite. But it is not without merit. For example, I think the information it provides on Anscombe's quartet is very useful. This example provides four data distributions. For each, the means and variances for both the X and Y variables are identical. The correlations between X and Y, and the regression lines, are also identical. This is the descriptive/inferential data that applies to each of the four graphs I have seen variations upon this in textbooks over the years, but typically they just show how different distributions can have the same mean and standard deviation. I think this example goes the extra mile by including r and the regression line. How to use in class: -Graphs aren't for babies. They can be an essential part of understanding your data. -Outliers are bad! -The original data is also included at the Wikipedia entry if you would like your students to create these graphs in class.

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...