I am busy prepping my statistics class for the fall (as well as doing a bunch of stuff that I should have done in June, but I digress). Most of my students are required to take statistics and are afraid of mathematics so I'm going to try to convince them to embrace statistics by showing them that more and more non-statsy jobs require data collection, data analysis, data driven decisions, program assessment, etc..  I find that my students are increasingly aware of the current job market as well as their student loan debt. As such, I think that students are receptive to arguments that  explain  how even a little bit of statistical knowledge can make them more attractive to potential employers. Here are some resources I have found to do just that.  This article by Susan Adams for Forbes lists the top ten skills employers are looking for in employees. Included in the top ten: "2. Ability to make decisions and solve problems 5. Ability to obtain and ...

The folks of TED have created ed.ted.com , a website that allows you to use their videos (or any video available via youtube) and create a lesson around the video. You can create an assessment quiz (and save your student's grades on the assessment). You can also create discussion boards and post your own commentary/links related to the content of the video. I know, right? There are several lessons that relate to statistics and research methods . Here is a shorter video that teaches the viewer how to assess  the quality of medical research , and here is a list of TED talks about Data Analysis and Probability  While the teaching of statistics and research methods are my jam, you can use any old video from youtube/TED ( like the many talks featuring psychology research ) and create an online lesson and assessment about the talk. Pretty cool! I think these could be use as bonus points, a quick homework assignment, and as a way to reiterate the more conceptual ideas surroun...

Nate Silver and Allison McCann (reporting for Five Thirty Eight, created graphs displaying baby name popularity over time.  The data and graphs can be used to illustrate bimodality, variability, medians, interquartile range, and percentiles. For example, the pattern of popularity for the name Violet illustrates bimodality and illustrates why measures of central tendency are incomplete descriptors of data sets: "Other names have unusual distributions. What if you know a woman — or a girl — named Violet? The median living Violet is 47 years old. However, you’d be mistaken in assuming that a given Violet is middle-aged. Instead, a quarter of Violets are older than 78, while another quarter are younger than 4. Only about 4 percent of Violets are within five years of 47." Relatedly, bimodality (resulting from the current trend of giving classic, old-lady names to baby girls) can result in massive variability for some names... ...versus trendy baby names th...

I am the mother of a boundary-testing two year old and my little guy likes to replicate his research findings with me all day long. We're currently trying to pull a sufficient n-size to test his hypothesis of whether or not I will ever let him eat dog food. I don't want to p-hack, but I'm pretty sure that that answer is no.

John Oliver demonstrates representative sampling by inviting three climate change deniers to debate 97 scientists who believe that global climate change is happening . Also, Bill Nye.

A study performed by The Public Religion Research Institute  used either a) a telephone survey or b) an anonymous web survey to question people about their religious beliefs and religious service habits. The researchers found that the telephone participants reported higher rates of religious behaviors and greater theistic beliefs. The figure below,  from a New York Times summary of the study , visualizes the main findings. The NYT summary also provides figures illustrating the data broken down by religious denomination. Property of the New York Times Participants also vary in their reported religious beliefs based on how they are surveyed (below, the secular are more likely to report that they don't believe in God when completing an anonymous online survey). Property of Public Religion Research Institute  This report could be used in class to discuss psychometrics, sampling, motivation to lie on surveys, social desirability, etc. Additionally, the sour...

This tongue-in-cheek  regression analysis found a way to predict the "Hipster Music Index" of a given artist by plotting # of Facebook shares of said artist's Pitchfork magazine review on they y-axis and Pitchfork magazine review score on the x-axis. If an artist falls above the linear regression line, they aren't "hipster". If they fall below the line, they are. For example, Kanye West is a Pitchfork darling but also widely shared on FB, and, thus demonstrating too much popular appeal to be a hipster darling (as opposed to Sun Kill Moon (?), who is beloved by both Pitchfork but not overly shared on FB). As instructors, we typically talk about the regression line as an equation for prediction, but Priconomics uses the line in a slightly different way in order to make predictions. Also, if you go to the source article, there are tables displaying the difference between the predicted Y-value (FB Likes) for a given artist versus the actual Y-value, which coul...