I am an Air Force Brat. Growing up, my dad used to talk about all of the services the USAF provides to our country and the world. It employs many  musicians , advances  airplane safety  for civilians, and conducts and sponsors plenty of research . This post will focus on the USAF's unique position to advance weather and climate science via data collection in big, honkin' airplanes that can fly through hurricanes.  Weather forecasting requires data. As reported by Debbie Elliot for NPR , the Air Force collects data that, specifically, will help us better predict severe weather and save lives.  Aside: This whole mission started on a bet: HOW TO USE IN CLASS: -I tell my students repeatedly that I'm not trying to turn them into the world's best statisticians. I'm trying to help them learn how to be themselves, with their interests and abilities, but fluent in statistical literacy. This lesson goes better when I can have examples of data jobs that aren't traditi...

It is April 18 in Erie, PA. It sleeted yesterday at my kid's soccer game. However, I know in my heart that Spring is coming. Every year, I get excited about the first crocuses and daffodils here in NW PA. Due to these hard winters followed by beautiful (if snowy) springs, I feel a certain kinship for the Japanese spring lovers who have been tracking the date of the cherry blossom blooms in Kyoto, Japan, for the last 1,200 years. Well, it hasn't always been tracked by humans; sometimes, modern humans have extrapolated this data. I'll get to that in a second. I learned about this data from Twitter user Robin Rohwer . She created this visualization for the data: https://twitter.com/RobinRohwer/status/1639097356657512449 She also shared where she found this data via NOAA , via  Yasuyuki Aono's website: http://atmenv.envi.osakafu-u.ac.jp/aono/kyophenotemp4/ . Go to the NOAA website and poke around. You can see notations referring to how the data was extrapolated over time an...

Eric Roston and Blacki Migliozzi put together some great data visualizations illustrating different factors that may or may not contribute to global climate change ( "What's Really Warming the World?" ). I couldn't capture it in this blog post, but the data is animated and interactive as to highlight change over time. Very slick. This got me thinking about multiple regression, which studies different variables (X 1 , X 2... ) that may or may not contribute to some outcome (Y), and how we can use this website as a conceptual example of multiple regression. Here, the graph features multiple "predictor"/X 1 , X 2 , X 3 , X 4 variables (greenhouse gasses, ozone, land use, aerosols) as well as the predicted/Y variable (global temperature). we can see the aerosols are likely a very poor predictor while greenhouse gasses are likely a good predictor. This page can also be used to explain plain old linear regression. This example compares one predictor/X...

The New York Time's maintains The Learning Network, which contains news content that fits well into a variety of classrooms teaching a variety of topics.  Recently, they shared a good stats example. They created curves illustrating global climate change over time. The top graph illustrates a normal curve, with normal temperature as the modal value. But as we shift forward in time, hot days become modal and the curves no longer overlap. Sort of like the classic illustration of what a small to medium effect size looks like in terms of distribution overlap.  This graph is part of the NYT's "What's going on in this graph?" series , which are created and shared in partnership with the American Statistical Association.

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 in 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. The first frost date is 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 the growing season in t...

A pretty video featuring Billy Barr, a gentleman that has been recording weather day in his corner of Gothic, Colorado for the last 40 years.  Billy Barr This brief video highlights his work. And his data provides evidence of climate change. I like this video because it shows how ANYONE can be a statistician, as long as... They use consistent data collection tools... They are fastidious in their data entry techniques... They are passionate about their research. Who wouldn't be passionate about Colorado?

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 report from Pew  highlights the differences in opinions between the average American versus members of the American Association for the Advancement of Science (AAAS). For various topics, this graph reports the percentage of average Americans or AAAS members that endorse each science related issues as well as the gap between the two groups. Below, the yellow dots indicate the percentage of scientists that have a positive view of the issue and the blue indicate the same data for an average American. If you click on any given issue, you see more detailed information on the data. In addition to the interactive data, this report by Funk and Rainie summarizes the main findings. You can also access the original report of this data  (which contains additional information about public perception of the sciences and scientists). This could be a good tool for a research methods/statistics class in order to convince students that learning about the rigors of the scientif...

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.

Variations upon a theme...good for spicing up a powerpoint...inspired by living in the #1 snowiest city (population > 100K, 2014) in the United States. property of xkcd.com https://thenib.com/can-t-stand-the-heat-4d5650fd671b