First day of class: Persuading students to treat statistics class as more than a necessary evil

No. Seriously. You will use data again at some point.

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 process information
6. Ability to analyze quantitative data
10. Ability to sell and influence others"

Erin Palmer at Business Insider provides a more direct endorsement of statistics by arguing that statistical skills are in demand, both within the context of explicitly statistical jobs but also in other career fields.

"Taking a statistics class in college is a good career move, even if your ultimate career goals have nothing to do with math. Before you roll your eyes, consider all the non-mathematical careers that use statistics. Executives, politicians, supply chain managers, entrepreneurs and marketers are among the many professionals who analyze data and statistics regularly."

Katie Bardaro's New York Times piece  STEM Skills Aren’t Just for STEM Majors argues that college students can be STEMy without having a STEM major:

"That being said, not all college students have the interest or ability to major in a STEM field. Another possibility is to major in a non-STEM field, but take some analytically focused courses like economics or statistics. Many jobs that previously didn't require analytic thought or data handling now do, and arming yourself with these skills is one way to get a leg up in the labor market."

ed.ted.com: TED video + assessment + discussion board

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 surround statistics.

From Not all scientific studies are created equal by David H. Schwartz

Also, if you are looking for more statsy videos to use with this tool, I do use a "video" label with this blog. Not all of the videos links I provide are hosted by youtube, but I bet that you could find most of these videos in youtube with just a little bit of Googling.

Do note:
1) In order to have full use of this site, you and your students do need to register.
2) I don't see a way to automatically upload assessment data into Blackboard or other learning management systems.

Five Thirty Eight's "How to Tell Someone’s Age When All You Know Is Her Name"

Nate Silver and Allison McCann 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, bimodaility (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 that have smaller interquartile ranges...

Your blogger exceeds the 75th percentile for Jessica Age. She considers herself a trendsetter, not old for her name.