Nature's "Policy: Twenty tips for interpreting scientific claims" by William J. Sutherland, David Spiegelhalter, & Mark Burgman

This very accessible summary lists the ways people fib with, misrepresent, and overextend data findings. It was written as an attempt to give non-research folk (in particular, law makers), a cheat sheet of things to consider before embracing/rejecting research driven policy and laws.

A sound list, covering plenty of statsy topics (p-values, the importance of replication), but what I really like is that they article doesn't criticize the researchers as the source of the problem. It places the onus on each person to properly interpret research findings. This list also emphasizes the importance of data driven change.

Baby Name Wizard's NameVoyager

Here is the Baby Name Wizard's NameVoyager, which provides illustrations of trends in baby names, using data from the 1880s to the present.

It is a good tool for demonstrating why graphs can be more engaging than tables when presenting data. When I use this in class, I compare the NameVoyager data display to more traditionally presented data from the the Social Security Agency. Additionally, I teach in a computer lab, so my students were able to search for their own names, which makes the example more self relevant.

Yup. I am one of many, many Jessicas that are around my age.

NPR's "Will Afghan polling data help alleviate election fraud?"

This story details the application of American election polling techniques to Afghanistan's fledgling democracy.

Essentially, international groups are attempting to poll Afghans prior to their April 2014 presidential elections as to combat voter fraud and raise awareness about the election.

However, how do researchers go about collecting data in a country where few people have telephones, many people are illiterate, and just about everyone is weary about strangers approaching them and asking them sensitive questions about their political opinions? The story also touches on issues of social desirability as well as the decisions  a researcher makes regarding the kinds of response options to use in survey research.

I think that this would be a good story to share with a cranky undergraduate research methods class that thinks that collecting data from the undergraduate convenience sample is really, really hard. Less snarkily, this may be useful when teaching multiculturalism or psychometrics.