We should teach with data sets representing ALL of our students. Why? You never know what example will stick in a student's head. One way to get information to stick in is by employing the self-reference effect .  For example, students who grew up in the country might relate to examples that evoke rural life. Like getting the first day of buck season off from school and learning how to watch out for deer on the tree line when you are going 55 MPH on a rural highway. Enter State Farm's data on the likelihood, per state, of a car accident claim due to collision with an animal (not specifically deer, but implicitly deer) . Indeed, my home state of Pennsylvania is the #3 most likely place to hit a deer with your car. State Farm shares its data per state: https://www.statefarm.com/simple-insights/auto-and-vehicles/how-likely-are-you-to-have-an-animal-collision I am also happy to share my version of the data , in which I turned all probability fractions (1 out of 522) into probabili...

Ok. Only some examples have to be profound. Sometimes, an example can break up a dry lesson like  z -scores.  This is my favorite z -score example . Ever. This current post may become my second favorite. The Pudding's Words Against Strangers is a game with four minute-long rounds. Each round asks for a type of word. Adjectives containing the letter "m." Verbs that contain an "r" and are precisely five letters long. That sort of prompt. Then you have one minute to type in as many of these words as possible. I recommend playing this on a computer, not a phone. If you are over 40. You are competing against one person on the internet.  After you play, your record is displayed as either: a) your over/under against the opponent b) your percentile score for everyone on the internet. Here is how I will use it in class. My students get into other games I've worked on in my classes ( Guess the Correlation ). I plan on asking my students to play this game, view their...

 So, as I sit here in December of 2020, I am being inundated with screenshots summarizing other people's Spotify listening data for the last year. Among the descriptive statistics provided by Spotify is recognition, in percentage form, of super fans who listened to A LOT of a given artist's music. Like my Tweep Dr. Sa-kiera Hudson , who loves George Michael. A whole bunch. Since my brain views reality as Stat Teaching Example or Not a Stats Teaching Example, I thought to myself, "Huh, wonder what Kiera's George Michael z-score is? It is a 3.72. And THAT made me think that this could be a funny homework question or in-class example when teaching z-scores.  But then I would need more examples, so I went to the original, very salty prompt for everyone posting their Spotify portions: And here are a bunch more for you to use in class. Throw them at your student for a class warm-up exercise. Use them for extra credit on an exam. Embrace the silly.  Admittedly, some of...

Nothing freaks out your students faster than a formula, right? Karly over at The Novice Professor shares some worksheets she created for her students to step them through a few of the most common Intro Stats formulas: standard deviation, z-scores, and correlation.  http://www.thenoviceprofessor.com/blog/teaching-statistical-methods-mostly-formula-free Reasons to use in class: 1) Statistics has its own anxiety scale. I think a lot of that anxiety comes from the math part of a stats scale. These hand outs allow you to introduce the math and formulas without ever using the math and formulas. 2) I am a big fan of introducing statistics conceptually then getting into the nitty gritty of calculation, interpretation of output, etc. I like the formula-free approach here in order to introduce the idea of what frequently used stats, like SD, are really doing.

The Ingrham, writing for The Washington Post, used data to investigate the claim that undocumented  immigrants are a large source of crime.  You may hit a paywall when you try to access this piece, FYI. Ingraham provides two pieces of evidence that suggest that undocumented immigrants are NOT a large source of crime. He draws on a  policy brief from the Cato Institute and a research study by Light and Miller  for his arguments. The Cato Institute policy brief   about illegal immigration and crime is actually part of a much larger study . It provides a nice conceptual example of a 3 (citizenship status: Native born, Undocumented Immigrant, Legal Immigrant) x 3 (Crime Type: All crimes, homicide, larceny) ANOVA. I also like that this data shows criminal conviction rates per 100K people, thus eliminating any base rate issues when comparing groups. From: https://www.washingtonpost.com/amphtml/news/wonk/wp/2018/06/19/two-charts-demolish-the-notion-that-i...

Gleaned from multiple sources (FB, Pinterest, Twitter, none of these belong to me, etc.). Remember, if your students can explain why a stats funny is funny, they are demonstrating statistical knowledge. I like to ask students to explain the humor in such examples for extra credit points (see below for an example from my FA14 final exam). Using xkcd.com for bonus points/assessing if students understand that correlation =/= causation What are the numerical thresholds for probability?  How does this refer to alpha? What type of error is being described, Type I or Type II? What measure of central tendency is being described? Dilbert: http://search.dilbert.com/comic/Kill%20Anyone Sampling, CLT http://foulmouthedbaker.com/2013/10/03/graphs-belong-on-cakes/ Because control vs. sample, standard deviations, normal curves. Also,"skewed" pun. If you go to the original website , the story behind this cakes has to do w...

Warning: This research and story include every paint-peeling obscenity in the book. Caution should be used when opening up these links on your work computer and you should really think long an hard before providing these links to your students. However, the research I'm about to describe 1) illustrates z-scores and 2) investigated regional usage of safe-for-the-classroom words like darn, damn, and gosh. So, a linguist, Dr. Jack Grieve  decided to use Twitter data to map out the use of different obscenities by county of the United States. Gawker picked up on this research and created a story about it . How can this be used in a statistics class? In order to quantify greater or lesser use of different obscenities, he created z-scores by county and illustrated the difference via a color-coding system. The more orange, the higher the z-score for a region (thus, greater usage) while blue indicates lesser usage. And, there are three such maps (damn, darn, and gosh) that are safe for us...

Now, I am immediately suspicious of a website entitled "MathIsFun" (I prefer the soft sell...like promising teaching aids for statistics that are, say, not awful and boring). That being said, t his app. from mathisfun.com  may be an alternative to going cross-eyed while reading z-tables in order to better understand the normal distribution. mathisfun.com With this little Flash app., you can select z-scores and immediately view the corresponding portion of the normal curve (either from z = 0 to your z, up to a selected z, or to the right of that z). Above, I've selected z = 1.96, and the outlying 2.5% of the curve is highlighted.  Now, this wouldn't work for a paper and pencil exam (so you would probably still need to teach students to read the paper table) but I think this is useful in that it allows students to IMMEDIATELY see how z-scores and portions of the of the curve co-vary.