Climate Central's The First Frost is Coming Later

So, this checks off a couple of my favorite requisites for a good teaching example: You can personalize it, it is contemporary and applicable, it illustrates a few different sorts of statistics. 

Climate Central wrote this article about first frost dates, and how those dates, and an increasing number of frost free days, create longer growing seasons. 

The overall article is about how frosty the US is becoming as the Earth warms. They provide data about the first frost is a number of US cities. It even lists my childhood hometown of Altoona, PA, so I think there is a pretty large selection of cities to choose from. Below, I've included the screen grab for my current home, and the home of Gannon University, Erie, PA.

First frost date illustrated with a line chart, but the chart also includes the regression line.

Data for frosty, chilly Erie, PA

The article also presents a chart that shows how frost is related to the length of growing season in the US. This graph is a good example as the y-axis shows the number of days above or below average growing season over time, starting with 1895 and going through 2015.

Both graphs also illustrate long term data collection, and illustrate archival data.

Using the Global Terrorism Database's code book to teach levels of measurement, variable types

A database code book is the documentation of all of the data entry rules and coding schemes used in a given data base. And code books usually contain examples of every kind of variable and level of measurement you need to teach your students during the first  two weeks of Intro Stats. You can use any code book  from any database relevant to your own scholarship as an example in class. Or perhaps you can find a code book particularly relevant to the students or majors you are teaching.

Here, I will describe how to use Global Terrorism Database’s code book for this purpose. The Global Terrorism Database is housed at University of Maryland and has been tracking national and international terrorism since 1970 and has collected information on  over 170, 000 attacks. So, the database in and of itself could be useful in class. But, I will focus on just the code book for  now, as I think this example cuts across disciplines and interests as all of our students are aware of terrorism and this particular code book doesn’t contain much technical jargon.

Here are just a few of the examples contained within it: Dichotomous, mutually exclusive response options ( Yes = 1, No = 0) can be found on page 14, in response to the question ”The violent act must be aimed at attaining a political, economic, religious, or social goal.”.

Another dichotomous response is used to indicate whether or not an attack was intended as a suicide attack (p. 26).

You can demonstrate nominal coding with the response options used to identify the country where the attack occured (Brazil = 30, Cambodia = 36, etc., on p. 17, ) or by looking at the coding scheme for different kinds of terrorist attacks (3 = Bombing/Explosion, p. 22).

Ratio scale of measurement is used to enter number of  perpetrators for a given terrorist act (p. 44). You can also discuss what is lost or gained by reporting (and analyzing) the cost of damages in either categorical and ordinal (number represents one of four ranges of dollars) or qualitative and ratio (enter the amount of loss in USD) format (on pages  49 and 50).

This code book and the actual data base also draw attention to attempts to better understand big, scary life problems via systematic data collection and analysis.  We need first responders and law enforcement officers and the bravery of regular citizens thrust into terrible situations in order to deal with terrorist events. We also need sharp, statistical minds to look for the patterns in these attacks in an attempt to prevent future attacks.

For a more advanced statistics class, you can also point out the naming conventions used in this database, and how naming conventions are good practice  and need to be sorted out prior to data collection.

Compound Interest's "A Rought Guide to Spotting Bad Science"

I love good graphic design and lists. This guide to spotting bad science embraces both. And many of the science of bad science are statistical in nature, or involve sketchy methods. Honestly, this could be easily turned into a  homework assignment for research evaluation.

This comes from the Compound Interest (@compoundchem), which has all sorts of beautiful visualizations of chemistry topics, if that is your jam.