I love internet-based teaching ideas. They are free and current. At least they were current when I first posted them, but some of my posts are ten years old.  Such is the case for my old post about the Baby Name Voyage r and how to use it to illustrate unimodal, and bimodal distributions. Instead, please go to NameGrapher to show your students how flash-in-the-plan trendy baby names, like my own, have an unimodal distribution: As opposed to bimodal distributions, which flag a name as a more classical name that enjoyed a resurgence, like Emma: When I use this in class, I frame it between names that were trendy once and names that were trendy one hundred years ago and are again trendy. As a mom to grade-school-aged kids, I have certainly noticed this as a trend in kid names. So many Lilies and Noras!  I also make sure my students understand that this information is gathered via Social Security Administration applications from the federal government, to back up another clai...

Alright, this is a 30-second long example for a) bimodal distributions and b) why measures of variability matter when we are trying to understand a mean. And that mean is...AGE OF DEATH. My inspiration for this tweet is: I’m just a girl, standing in front of the internet, asking it to understand that historical life expectancies doesn’t mean most people died at 45 but rather that infant mortality was super high and pulled down the average. — Angelle Haney Gullett (@CityofAngelle) January 12, 2022 Gullett refers here to the commonly held belief that if the mean life span Back In The Day was 45, or thereabout, everyone was dying around 45. NOT SO. Why? The short answer is no. Broadly speaking, there were two choke point of human mortality. Younger than 5, and again around 50. If you made it through those, barring accidents, you likely had what was a normal lifespan of ~65-70 years. And this is why I’m no fun at parties 😂 — Angelle Haney Gullett (@CityofAngelle) January 12, 2022 OK. An...

I apologize in advance if you are pandemiced out. It is just that my brain won't stop seeing stats examples in information related to the COVID-19 pandemic. For instance, researchers are looking back at the 1918 Flu Pandemic in order to forecast how social distancing (or lack thereof) will affect mortality rates now. And these patterns, as illustrated by National Geographic, demonstrate different data distribution shapes . The data comes from a reputable source, is scaled to deaths per 100,000 as to allow for comparison, and the distributions are related to very important data. Other lessons your students can learn from this data: This is what good scicomm looks like. Also, sometimes a good data visualization is better than an accurate-yet-filled-with-jargon version of the same information. For instance, much has been shared about NYC vs St. Louis in terms of timing of quarantine. Here is the comparison yet again, but in an easier-to-follow description: There is a ton of...

This is a fun question perfect for that first or second chapter of every intro stats text. The part with data distributions. And it works for either the 1) beginning of the Fall semester and, therefore, football season or 2) the beginning of the Spring semester and, therefore, the lead-up to the Superbowl. Anyway,  Ben Jones   tweeted a few bar chart distributions that illustrate different descriptive statistics for NFL players. https://twitter.com/DataRemixed/status/1022553248375304193  He, kindly, provided the answers to his quiz. How to use it in class: 1) Bar graphs! 2) Data distributions and asking your students to logic their way through the correct answers...it makes sense that the data is skewed young. Also, it might surprise students that very high earners in the NFL are outliers among their peers. 3) Distribution shapes: Bimodal because of linebackers. Skewed because NFL players run young and have short careers. Normal data for height because even...

Nate Silver and Allison McCann (reporting for Five Thirty Eight, 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, bimodality (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 th...