Here is an article from the Chronicle of Higher Education ( .pdf  in case you hit the pay wall) about what happens when university PR promotes research findings in a way that exaggerates or completely misrepresents the findings. Several examples of this are included (Smelling farts cures cancer? What?), including empirical study of how health related research is translated into press releases ( Sumner et al. , 2014). The Sumner et al. piece found, that among other things, that 40% of the press releases studied contained exaggerated advice based upon research findings. I think that this is an important topic to address as we teach our student not to simply perform statistical analyses, but to be savvy consumers of statistics. This may be a nice reading to couple with the traditional research methods assignment of asking students to find research stories in popular media and compare and contrast the news story with the actual research article. If you would like more di...

Rustid, a Reddit redditor, performed a research study in order to determine the proportions of peanut butter contained in different types of Reese's Peanut Butter candies. For your perusal, here is the original  reddit thread  (careful about sharing this with students, there is a lot of talk about how the scales Rustid used are popular with drug dealers), photo documentation via  Imgur , and a  Buzzfeed article  about the experiment. Rustid documented the process by which he carefully extracted and measured the peanut butter content of nine different varieties of Reese's peanut butter and chocolate candies. See below for a illustration of how he extracted the peanut butter with an Exact-o knife and used electronic scales for measurements. http://imgur.com/a/wN6PH#SUhYBPx Below is a graph of the various proportions of peanut butter contained within each version of the Reese's Peanut Butter Cup. http://imgur.com/a/wN6PH#SUhYBPx This example...

I love reddit . I really love the sub-reddit r/dataisbeautiful . Various redditors contribute interesting graphs and charts from all over the interwebz. I leave you to figure out how to use these data visualizations in class. If nothing else, they are highly interesting examples of a wide variety of different graphing techniques applicable to different sorts of data sets. In addition to interesting data visualizations, there are usually good discussions (yes, good discussion in the internet!) among redditors about what is pushing the presented findings. Another facet of these posts are the sources of the data. There are many examples using archival data, like this chart that used social media to estimate sports franchise popularity , Users also share interesting data from more traditional sources, like APA data on the rates of Masters/Doctorates awarded over time and user rating data generated by IMDB ( here, look at the gender/age bias in ratings of the movie Fifty Shades of Gr...

This semester (SP 15), I taught an Honors section of Psychological Statistics for the first time. In this class, I decided to take my students to a minor league baseball game ( The Erie Seawolves , the Detroit Tiger's AA affiliate) in order to teach my students a bit about 1) applied statistics and data collection as well as 2) selecting the proper operationalized variable when answering a research question. Students prepared for the game day activity via a homework assignment they completed prior to the game. For this assignment, students learned about a few basic baseball statistics (batting average (AVG), slugging (SLG), and on-base plus slugging (OPS)). They looked up these statistics for a random Seawolves' player (based on 2014 data) and learned out to interpret these data points. They also read an opinion piece on why batting averages are not the most informative piece of data when trying to determine the merit of a given player. The opinion piece tied this exe...

One way to engage students in the classroom is by bringing in debates and real world examples. Sometimes, such debates take place largely over social media. A Facebook question du jour: Is "free-range" (letting your kids go out side, walk to the store, etc. without supervision) a good way to build independence or child neglect? Anecdotes abound, but how safe is your kid when they are out on their own? What kind of data could help us answer this question objectively? http://www.nytimes.com/2015/03/20/opinion/the-case-for-free- range-parenting.html The first piece of information comes from an opinion piece by Clemens Wergin from the New York Times ( .pdf in case of pay wall). Wergin describes how free range parenting is the norm in Germany and contrasts American attitudes to German attitudes, providing a quick example of multicultralism (and why we should never assume that the American attitude towards something is the only opinion). He then  provides data that explain...

I love counterintuitive data that challenges commonly held beliefs. And there is a lot of counterintuitive health data out there (For example, data questioning the health benefits associated with taking vitamins  or data that lead to a revolution in how we put our babies to sleep AND cut incidents of SIDS in half ). This story by Aschwanden for fivethirtyeight.com discusses efficacy data for various kinds of cancer screening. Short version of this article: Early cancer screening detects non-cancerous lumps and abnormalities in the human body, which in turn leads to additional and evasive tests and procedures in order to ensure that an individual really is cancer-free or to remove growths that are not life-threatening (but expose an individual to all the risks associated with surgery). Specific Examples: 1) Diagnosis of thyroid cancer in South Korea has increased. Because it is being tested more often. However, death due to thyroid cancer has NOT increased (see figure below)...

Elahe Izadi, writing for the Washington Post, did a report on this article by Eichstaedt et. al, (2015) . The original research analyzed tweet content for hostility and noted the location of the tweet. Data analysis found a positive correlation between regions with lots of angry tweets and the likelihood of dying from a heart attack. The authors of the study note that the median age of Twitter users is below that of the general population in the United States. Additionally, they did not use a within-subject research design. Instead, they argue that patterns in hostility in tweets reflect on underlying hostility of a given region. An excellent example of data mining, health psychology, aggression, research design, etc. Also, another example of using Twitter, specifically, in order to engage in public health research ( see this previous post detailing efforts to use Twitter to close down unsafe restaurants ).

This article and figure from Harry Enten (reporting for fivethrityegiht) provides informative and horrifying data on the median last day of measurable snow in different cities in America. (Personally, I find it horrifying because my median last day of measurable snow isn't until early April). This article provides easy-to-understand examples of percentiles, interquartile range, use of archival data, and median. Portland and Dallas can go suck an egg.

Weber and Silverman's article for the Wall Street Journal has lots of good psychy/stats information  ( here is a .pdf of the article if you hit a pay wall ). I think it would also be applicable to health and I/O psychology classes. The graph below summarizes the main point of the article: Certain occupations have a greater likelihood of obesity than others (a good example of means, descriptive statistics, graphs to demonstrate variation from the mean). As such, how can employers go about increasing employee wellness? How does this benefit an organization financially? Can data help an employer decide upon where to focus wellness efforts? The article goes on to highlight various programs implemented by employers in order to increase employee health (including efficacy studies to test the effectiveness of the programs). In addition to the efficacy research example, the article describes how some employers are using various apps in order to collect data about employee health and...

io9.com Das and Biller, reporting for io9.com , shared several good examples of bad graphs. The graphs are bad for a variety of reasons. I have highlighted a few below. Non-traditional display of data that create the illusion that the opposite of the truth is true: Note the y-axis is flipped (0 at the top...huh?), so murders have actually INCREASED since "Stand Your Ground".  Cherry picking data: Confusing data presentation: I think that this could be fun to use in class as a discussion piece to pick apart bad graphs, so that your students 1) think critically about all graphs and figures they see and 2) learn how to make truthful graphs. Another fun way to use this in class would be to present these graphs to your students and then ask them to create APA style manual compliant graphs of the same data.

Taylor, writing for Mashable , describes what happens when carefully conducted public health research (published in the  Journal of the American Medical Association ) becomes attention grabbing and poorly represented click bait. Data published in JAMA (Case, Burwick, Volpp, & Patel, 2015) tested the step-counting reliability of various wearable fitness tracking devices and smart phone apps (see the data below). In addition to checking the reliability of various devices, the article makes an argument that, from a public health perspective, lots of people have smart phones but not nearly as many people have fitness trackers. So, a way to encourage wellness may be to encourage people to use the the fitness capacities within their smart phone (easier and cheaper than buying a fitness tracker). The authors never argue that fitness trackers are bad, just that 1) some are more reliable than others and 2) the easiest way to get people to engage in more mindful walking...

Warning: This isn't an easy story to listen to, as it is about life expectancy and terminal cancer (and how doctors can best convey such information to their patients). Most of this news story is dedicated to training doctors on the best way to deliver this awful news.   But Aronczyk, reporting for NPR, does tell a story that provides a good example of high-stakes applied statistics . Specifically, when explaining life expectancy to patients with terminal cancer, which measure of central tendency should be used? See the quote from the story below to understand where confusion and misunderstanding can come from measures of central tendency. " The data are typically given as a median, which is different from an average. A median is the middle of a range. So if a patient is told she has a year median survival, it means that half of similar patients will be alive at the end of a year and half will have died. It's possible that the person's cancer will advance quic...

http://www.washingtonpost.com/blogs/the-fix/wp/2014/09/29/ believe-it-or-not-some-members-of-congress-are-accountable-to-voters/ Philip Bump (writing for The Washington Post) illustrates the linear relationship between a U.S. House of Representative Representative's politics and their home district's politics. Yes, this is entirely intuitive. However, it is still a nice example of correlations/linear relationships for the reasons described below. Points for class discussion: 1) How do they go about calculating this correlation? What are the two quantitative variables that have been selected? Via legislative rankings (from the National Journal) on the y-axis and voting patterns from the House member's home district on the x-axis. 2) Several outliers' (perhaps not mathematical outliers, but instances of Representative vs. District mismatch ) careers are highlighted within the news story in order to explain why they don't align as closely with their distric...