TED isn't the only source of videos for teaching statistics . The Economist also makes animated videos that are lousy with data. One easy, no-pay-wall source for such videos is The Economists Videographic playlist on YouTube  (there is a limit on number article views/month at their website ). One really statsy video from The Economist that I've featured previously on this blog explains the real life implications for Type I/Type II error in research (and, specifically, how it leads to errors in published research ). The other videos may not be as directly related to the teaching of statistical topics, but they do illustrate data. Topics range from American union membership trends to this video about world population growth . As you may have inferred from the source, many of these videos focus on national and global economic information, but all of the videos do present data that you can integrate into your classes. Some are more applicable to teaching statistics: This vid...

I am a big fan of Pew Research Center . They collect, share, and summarize data about a wide variety of topics. In addition to providing very accessible summaries of their findings, they also provide more in-depth information about their data collection techniques, including original materials used in their data collection and very through explanations of their methods. One topic they collect Pew studies is religion and attitudes (religious and secular) held by people of different religions. And it got me thinking that I could use their data in order to explain within and between group differences at the heart of a conceptual understanding of ANOVA. Specifically, Pew gathered data looking at between-group differences in beliefs in global climate change by religion ... Chart created by Pew Research ... and belief in climate change within just Catholics, divided up by political affiliation. Chart created by Pew Research The questionnaires differed slightly for the...

This story describes an 1) an efficacy study that 2) touches on some I/O/Health psychology research and 3) has gained the unwanted attention of government regulatory agencies charged with protecting research participants.   The study described in this story is an efficacy study that questions a decision made by the 2003 Accreditation Council for Graduate Medical Education. Specifically, this decision capped the number of hours that first-year medical student can work at 80/week and a maximum shift of 16 hours. The PIs want to test whether or not these limits improve resident performance and patient safety. They are doing so by assigning medical students to either 16-hour maximum shifts or 30-hour maximum shifts. However, the research participants didn't have the option to opt out of this research. Hence, an investigation by the federal government. So, this is interesting and relevant to the teaching of statistics, research methods, I/O, and health psychology for a numbe...

I just had a baby. Arthur Francis joined our family last week. Don't mind the IV line on his head, he is a happy, chubby little boy. Now, I am the mother of a new born and a toddler. And I have certainly been inundated by the formula versus breast feeding debate. In case you've missed out on this, the debate centers around piles and piles of data that indicate that breast fed babies enjoy a wealth of developmental outcomes denied to their formula fed peers. Which means there is a lot of pressure to breast feed (and some women feel a lot of guilt when they can't/do not want to breast feed). However, the data that supports breast feeding also finds that breast feeding is much more common among  educated, wealthy white women with high IQs. And being born to such a woman probably affords a wealth of socioeconomic advantages beyond simply breast milk. These issues, as well as mixed research findings, are reviewed in Emily Oster's "Everybody calm down about brea...

Data has been used to learn a bit more about the age old observation that books are always better than the movies they inspire. Fivethirtyeight writer Walk Hickey gets down to the brass tacks of this relationship by exploring linear relationships between book ratings and movie ratings.  The biggest discrepancies between movie and book ratings were for "meh" books made into beloved movies (see "Apocalypse Now"). How to use in class: -Hickey goes into detail about his methodology and use of archival data. The movie ratings came from Metacritic, the book ratings came for Goodreads. -He cites previous research that cautions against putting too much weight into Metacritic and Good reads. Have your students discuss the fact that Metacritic data is coming from professional movie reviewers and Goodreads ratings can be created by anyone. How might this effect ratings? -He transforms his data into z-scores. -The films that have the biggest movie:book rati...

The writer helped out a PhD candidate by participating in his research, and then described the research process for io9.com readers . I like this because it is describes the research process purely from the perspective of the research participant who doesn't know what the exact hypothesis is. This could be useful for explaining what research participation is like for introductory students. You could used it in a methods class by asking the students to figure out why they used the procedures that they did, and what procedures and scales she describes in her narrative. She describes the informed consent, a personality scale (what do you think the personality scale was trying to assess?), and rating stimuli in two ways (brain scan as well as paper and pencil assessment...why do you think they needed both?) Details to Like: -She is participating is psychology research (neruo. work that may benefit those with PTSD someday) -She describes what is entailed when wearing an elect...

Found this gem, "Guess the Correlation" , via the subreddit r/statistics . The redditor who posted this resource (ow241) appears to be the creator of the website. Essentially, you view different scatter plots and try to guess r . Points are rewarded or taken away based on how close you are to true  r . The game tallies your average amount of error as well. It is way more addictive than it sounds. I think that accuracy increases with time and experience. True r for this one was .49. I guess .43, which isn't so bad. I think this is a good way for statistics instructors to procrastinate. I think it is also a good way to help your students build a more intuitive ability to read scatter plots and predict the strength of linear relationships.