http://io9.com/i-fooled-millions-into-thinking-chocolate-helps-weight-1707251800 This story demonstrates how easy it is to do crap science, get it published in a pay-to-play journal, and market your research (to a global audience). Within this story, there are some good examples of Type I error, p -hacking, sensationalist science reporting, and, frankly, our obsession with weight and fitness and easy fixes—also, chocolate. Here is the original story, as told to io9.com by the perpetrator of this very conscientious fraud, John Bohannon . Bohannon ran this con to expose just how open to corruption and manipulation the whole research publication process can be ( BioMed Central scandal , for another example), especially when it just the kind of research that is bound to get a lot of media attention ( LaCour scandal , for another example). Bohannon set out to "demonstrate" that dark chocolate can contribute to weight loss. He ran an actual study ( n = 26). He went on a ...

There are a number of TED talks that apply to research methods and statistics classes. First, there is this TED playlist entitled The Dark Side of Data . This one may not be applicable to a basic stats class but does address broader ethical issues of big data, widespread data collection, and data mining. These videos are also a good way of conveying how data collection (and, by extension, statistics) are a routine and invisible part of everyday life. This talk by Peter Donnelly discusses the use of statistics in court cases, and the importance of explaining statistics in a manner that laypeople can understand. I like this one as I teach my students how to create APA results sections for all of their statistical analyses. This video helps to explain WHY we need to learn to report statistics, not just perform statistics. Hans Rosling has a number of talks (and he has been mentioned previously on this blog, but bears being mentioned again). He is a physician and conveys his passion...

I am going up for both Rank and Tenure this fall. Within my applications for both, I will argue that this blog constitutes service to my profession. I have evidence of this: The blog has 50,000+ page views from 115 countries. I have 271 Twitter followers. So, I can successfully argue that someone other than my dad is reading the blog (Hi, Dad!). However, I think that more compelling evidence of service to my profession would come in the form of brief testimonials from its readers. If you have a few free moments, please consider writing a brief email that describes, maybe, your favorite blog post, why you enjoy this blog, how you think this blog contributes to the teaching of statistics and research methods, or mention a specific blog post or two that you've integrated into your own class. Do you students seem to enjoy any of the materials I've shared here? Have you recommended the blog to peers? You get the idea. Think you can help me out? If so, please shoot me an emai...

This blog posting by Dr. Randy McCarthy discusses best practices in organizing/naming conventions for data files. These suggestions are probably more applicable to teaching graduate students than undergraduates. They are also the sorts of tips and tricks we use in practice but rarely teach in the classroom (but maybe we should). Included in Randy's recommendations: 1) Maintain consistent naming conventions for frequently used variables (like scale items or compiled scales that you use over and over again in your research). Then create and run the same syntax for this data for the rest of your scholarly career. If you are very, very consistent in the scales you use and the data analyses your run, you can save yourself time by showing a little forethought. 2) Keep and guard a raw version of all data sets. 3) Annotate your syntax. I would change that to HEAVILY annotate your syntax. I even put the dates upon which I write code so I can follow my own logic if I have to let a d...

This little questionnaire will provide you with a) the ordinal value of your name for your sex/year of birth and then generate b) a bunch of other names from various decades that share your name's ordinal. Not the most complex example, but it does demonstrate ordinal data. Me and all the other 4th most popular names for women over the years. Additionally, this data is pulled from Social Security, which opens up the conversation for how we can use archival data for...not super important interactive thingies from Time Magazine? Also, you could pair up this example with other interactive ways of studying baby name data ( predicting a person's age if you know their name , illustrating different kinds of data distributions via baby name popularity trends ) in order to create a themed lesson that would correspond nicely to that first/second chapter of most undergraduate stats textbooks in which you learn about data distribution and different types of data.

How do we count the number of poor people in America? How do we operationalize "poor"? That is the psychometric topic of this opinion piece from the New York Times  ( .pdf of same here ). This article outlines several ways of defining poor in America, including: 1)"Jencks’s methodology is simple. He starts with the official 2013 United States poverty rate of 14.5 percent. In 2013, the government determined that 45.3 million people in the United States were living in poverty, or 14.5 percent of the population.Jencks makes three subtractions from the official level to account for expanded food and housing benefits (3 percentage points); the refundable earned-income tax credit and child tax credit (3 points); and the use of the Personal Consumption Expenditures index instead of the Consumer Price Index to measure inflation (3.7 percentage points)." 2)  " Other credible ways to define poverty  paint a different picture. One is to count all those living ...

Scott Janish loves beer, statistics, and blogging (a man after my own heart). His blog discusses home brewing as well as data related to beer. One of his statsy blog posts  looked at the relationship between average alcohol by volume for a beer style (below, on the x-axis) and the average rating (from beeradvocate.com , y-axis). He found, perhaps intuitively, a positive correlation between the average Beer Style review for a type of beer and the moderate alcohol content for that type of beer. Scott was kind enough to provide us with his data set, turning this into a most teachable moment. http://scottjanish.com/relationship-of-abv-to-beer-scores/ How to use it in class: 1) Scott provides his data. The r is .418, which isn't mighty impressive. However, you could teach your students about influential observations/outliers in regression/correlation by asking them to return to the original data, eliminate the 9 data points inconsistent with the larger pattern, and reanalyze th...

51% of the babies born in the US are male. Why? For a long time, people just assumed that the skew started at conception. Then Steven Orzack decided to test this assumption. He (and colleagues) collected sex data from abortions, miscarriages, live births (30 million records!), fertility clinics (140,00 embryos!), and different fetal screening tests (90,000 medical records!) to really get at the root of the sex skew/conception assumption. And the assumption didn't hold up: The sex ratio is pretty close to 50:50 at conception. Further analysis of the data found that female fetuses are more likely to be lost during pregnancy. Original research article here . Richard Harris' (reporting for NPR) radio story and interview with Orzack here . Use this story in class as a discussion piece about long held (but never empirically supported) assumptions in the sciences and why we need to conduct research in order to test such assumptions. For example: 1) Discuss the weaknesses of previo...

This animated short from National Geographic touches on averages, median, mode, sampling, and the need for cross-cultural research. When defining the typical (modal) human, the video provides good examples of when to use mode (when determining which country has the largest population) and when to use median (median age in the world). It also illustrates the need to collect cross-cultural data before making any broad statements about typicality (when describing how "typical" is relative to a population).

This story describes how psychologists used content analysis to provide evidence that Shakespeare indeed authored the play Double Falsehood. The play in question has been the subject of literary dispute for hundreds of years. It was originally published by Lewis Theobold in 1727. Theobold claimed it was based on unpublished works by Shakespeare. And literary scholars have been debating this claim ever since. Enter two psychology professors, Boyd and Pennebaker. They decided to tackle this debate via statistics. They conducted a content analysis Double Falsehood as well as confirmed work by Shakespeare. What they tested for: "Under the supervision of University of Texas psychology professors Ryan L. Boyd and James W. Pennebaker, machines churned through 54 plays -- 33 by Shakespeare, nine by Fletcher and 12 by Theobold -- and tirelessly computed each play's average sentence-length, quantified the complexity and psychological valence of its language, and sussed out the ...

http://www.theonion.com/articles/study-finds-those-with-deceased-family-members-at,38463/ Man, I love the fact that when The Onion does an article about pretend research , they don't skimp on the details. This story includes the journal (NEJM), n -size (85,000), research design (longitudinal), a lead author, covariates (race, nationality, etc.) as well as a replication study. I like to think that the person who writes these articles paid really close attention in a statistics class or a research methods class and remembers just enough to be a smart ass about the research process. From the article: I used this just last week as a review example of correlation =/= causation. The students liked it. Mission accomplished.

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...

This report from Pew  highlights the differences in opinions between the average American versus members of the American Association for the Advancement of Science (AAAS). For various topics, this graph reports the percentage of average Americans or AAAS members that endorse each science related issues as well as the gap between the two groups. Below, the yellow dots indicate the percentage of scientists that have a positive view of the issue and the blue indicate the same data for an average American. If you click on any given issue, you see more detailed information on the data. In addition to the interactive data, this report by Funk and Rainie summarizes the main findings. You can also access the original report of this data  (which contains additional information about public perception of the sciences and scientists). This could be a good tool for a research methods/statistics class in order to convince students that learning about the rigors of the scientif...

Kamenetz (reporting for NPR) talks about her book , Test , which is about the extensive use of standardized testing in our schools. Largely, this is a story about the impact these tests have had on how teachers instruct K-12 education in the US. However, a portion of the story discusses alternatives to annual testing of every student. Alternatives include using sampling to assess a school as well as numerous alternate testing methods (stealth testing, assessing child emotional well-being, portfolios, etc.). Additionally, this story touches on some of the implications of living in a Big Data society and what it is doing to our schools. I think this would be a great conversation starter for a research methods or psychometric course (especially if you are teaching such a class for a School of Education). What are we trying to assess: Individual students or teachers or schools? What are the benefits and short comings of these different kinds of assessments? Can you students come up with...

I'm an SPSS girl. I sit in my Psychology Department ivory tower and teach Introduction to Statistics via SPSS. SPSS isn't the only way to do the statistics. In fact, it is/has been losing favor among "real" statisticians. I recently had a chat with a friend who has a Ph.D. in psychology and works as a statistician. She told me that statsy job postings rarely ask for SPSS skills. Instead, they are seeking people who know R and/or Python. In order to better help our data-inclined students find work, I've gathered some information on learning R and Python. This probably isn't for every student. This probably isn't for 90% of our students. However, it may be helpful for an outstanding undergraduate or graduate student who is making noise like they want a data/research oriented career. Alternately, I think that an R class could be a really cool upper-level undergraduate elective for a select group of students. Also, if anyone is brave enough to teach thei...

Khan has been providing high-quality videos explaining...indeed...everything for a while now. Among everything are Probability and Statistics. Recently, they reorganized their content and added assessment tools as part of their #youcanlearnanything campaign in order to create self-paced lessons that are personalized to the user and include plenty of videos (of course) and personalized quizzes and feedback. 1) It requires the creation of a free account and selection of a learning topic (the screen shots below are from the Statistics and Probability course). 2) When you start a topic, you take pre-test to assess your current level. This assessment covers simple chart reading, division, and multiplication required for more advanced topics. If you struggle with this, Khan provides you with more material to improve your understanding of these topics. 3) After you complete the assessment, you receive your lesson plan. It includes the topic you select plus an additional introductory ...

UPDATE: The link listed below is currently not working. I've talked to Ariana Giorgi about this, and she is working to get her graph up and running again via Bloomberg. She was kind enough to provide me with a provide me with alternate URLs to the interactive scatter plot  as well as a link to the original text of the story . Ariana is doing a lot of interesting work with data visualizations, follow her on Twitter or hit up her website . _______________________________________________________________________________ This scatter plot (and accompanying news story from Bloomberg News)  demonstrates what a non-existent linear relationship looks like. The data plots CEO pay on the x-axis and stock market return for that CEO's organization on the y-axis. I could see where this graph would also be useful in an I/O course in discussions of (wildly unfair) compensation, organizational justice, etc. http://www.bloomberg.com/bw/articles/2014-07-22/for-ceos-correlation...

Do you follow  Saturday Morning Breakfast Cereal  on  Facebook  or  Twitter ? Zach Weinersmith's hilarious web comic series frequently touches upon science, research methods, data collection, and statistics. Here are some such comics. Good for spiffing up a power point, spiffing up an office door (the first comic adorns mine) or ( per this post ) testing understanding of statistical concepts. http://www.smbc-comics.com/?id=2080...also a good example of the availability bias! http://www.smbc-comics.com/?id=3129 http://www.smbc-comics.com/?id=3435 http://www.smbc-comics.com/?id=1744 http://www.smbc-comics.com/?id=2980 http://smbc-comics.com/index.php?id=4084 http://www.smbc-comics.com/comic/2011-08-05 https://www.smbc-comics.com/index.php?id=4127 http://smbc-comics.com/comic/false-positives https://www.smbc-comics.com/comic/relax

Pew Research went around the globe and asked folks in 40 different countries if a variety of different behaviors qualified as "Unacceptable", "Acceptable", or "Not a moral issue". See below for a broad summary of the findings. Summary of international morality data from Pew The data on this website is highly interactive...you can break down the data by specific behavior, by country, and also look at different regions of the world. This data is a good demonstration of why graphs are useful and engaging when presenting data to an audience. Here is a summary of the data from Pew.  It nicely describes global trends (extramarital affairs are largely viewed as unacceptable, and contraception is widely viewed as acceptable). How you could use this in class. 1) Comparison of different countries and beliefs about what is right, and what is wrong. Good for discussions about multiculturalism, social norms, normative behaviors, the influence of religion ...