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...

 Hey. Most of you have never met me and only read my words on this blog, so I thought it would be fun to share a recording of a talk I gave at Murray State University in October of this year .  Not only do you get to see/hear me in action, I think this talk does a great job of summing up my approach to statistics and what I want my students to get out of my class. If you agree with my approach, may I gently suggest that you sign yourself up to get updates on  my forthcoming WW Norton Psychological Statistics textbook: https://seagull.wwnorton.com/l/710463/2023-10-26/2tp3nt

Spotify generates gobs of data about music.  Most people have seen the end-of-the-year data Spotify generates for each user about their listening patterns . Most people don't know that Spotify also generates a lot of data about individual songs. Some of it is straightforward: tempo, genre, length. However, Spotify also has its own niche way of quantifying songs: Danceability. Accousticness. Here is a whole list of their variables and descriptions from researchers at CMU:  https://www.stat.cmu.edu/capstoneresearch/315files_s23/team23.html What does this mean for a stats teacher? You have access to highly personalizable data sets, rooted in music, with gobs and gobs of variables for each song...or artist...or album...or year of release...or genre (like, so many ways to divide up your data).  For instance,  I created a data set with Spotify data for 1989 and 1989 (Taylor's Version) to teach paired  t -tests . How do Taylor's re-recordings compare to the originals?...

Ok, more Taylor Swift data for you. DID YOU KNOW that Spotify collects buckets and buckets of data about each and every song it provides (see:  https://www.spotify-song-stats.com/about ) So, I downloaded this information for 1989 and 1989 (Taylor's Version). So I could test for any differences between the recordings. Like, with data, not with my feelings and emotions. Specifically with a paired t -test. I get it. The sample sizes are very small. However, the data is still interesting. It makes sense that the tempo hasn't changed. Like, she did slow down or speed up anything. And that is super NS with an itty-bitty effect size. It is also interesting that acousticness has decreased. These are more heavily produced versions of the same songs (IMO), and while this change didn't achieve significance, it is a moderate effect size.  ANYWAY, you aren't really here for this information. You are here for data to share with your classes, yes? I'm here to help you teach your s...

Holy smokes. I am almost done with my textbook, Statistics for Everyone. It is a Psych. Stats. textbook. Like, the project started in 2019. It really started when I started my blog in 2012, but my awesome, supportive team at Norton and I started working on this textbook in 2019. I have been supported every step of the way by my editorial team. Norton understood my vision: An engaging, supportive, joyful stats textbook. It is filled with science silliness, and pop culture. Something that prepares students to become statisticians AND citizens in an increasingly data-driven world. Something I created out of my experiences teaching smart, hardworking, sometimes hesitant statistics students at Gannon University since 2009.  I am so excited to share it with you all. It is going to be something special. I want to help you teach your statistics class, and I want to help your students understand statistics.  If you would like to pre-register for an exam copy of the book, please go to t...

In what is a common occurance for this blog, it all started with a Tweet. A very punny Tweet https://twitter.com/ESPNFantasy/status/1716216331752624509 It begs the question: How are various indicators of Kelce's performance influenced by the presence or absence of one Taylor Swift? What she is steadily attending games this fall, we'll have to wait and see if her international tour, starting 11/7, changes that. Regardless, I'll update THIS SPREADSHEET over the season so you can run all of the independent t-tests you want with your students.  AND SOMEDAY I WILL UPDATE THIS SPREADSHEET TO INCLUDE WHETHER OR NOT THEIR CHILDREN ATTEND I SWEAR IT IS COMING.

1. I assure you, your traditional college-aged students know who Mr. Beast is. 2. If you don't know who he is, just Google him. 3. His real name is Jimmy so that's what I'll call him for the remainder of the post because while I respect his work and can't handle writing/referring to an adult human who isn't a wrestler as Mr. Beast again. Anyway, Jimmy shared, via Twitter (it is still Twitter) that he had done some A/B testing on his clips. A story in two Tweets.  https://twitter.com/MrBeast/status/1699460698726613343 https://x.com/MrBeast/status/1699460698726613343?s=20 This story made the rounds because Mr. Beast is such a famous YouTuber . How can you use this example in class? 1. Introduce A/B testing, and how some of the techniques used by professional statisticians are actually pretty straightforward application of basic statistics tests (here, paired t -test). 2. Conduct a paired t -test: I made up some pretend data that imitates these findings . 3. Review the...

Y'all. This is a statsy example featuring sensation and perception, developmental, and neuroscience.  The study found that post-partum, but not pregnant, women, saw faces where there were no faces (pareidolia illusion) . It is attributed to the endogenous oxytocin bump women experience after they have babies. Here is a link to Newsweek's treatment of the study and the actual study . Here are some examples of the photos used in the experiment. They are so dear because I see faces. I think my favorite is the clothes washer. Anyway, the researchers used pregnant women, post-partum women, and a control group and measured how often they saw faces. How to use 1. There is a good ol' Mann-Whitney U in this study. Making this the first ever Mann-Whitney U featured on the blog. 2. The researchers used OSF, and the data is available . 3. I like the growing trend of pairing newer and older data visualizations. Here, bar graphs and jitter plots are used to illustrate the same data, and...

I am in a hotel lobby in Portland, OR. I am attended Society for the Teaching of Psychology's Annual Conference on Teaching. I did a talk with my friend Jenny Kunz on syllabus redesign. We found that incorporating graphic design principles in syllabi improve retention of syllabus information.   Anyway, that reminded me of the recent passing of Burkey Belser. Who is that? He is the graphic designer who created the the labels on each and every food item sold in America. I learned about his passing from this remembrance in NPR. IT IS A FREQUENCY TABLE, Y'ALL. I never thought about it this way until, like, a week ago. After seeing these and using these for years and years. Okay, first, let's just take a moment to admire one of Belser's professional head shots. RIGHT?! Anyway, I had never heard of  Belser until I came across this remembrance on NPR: How to use in class: 1. Frequency table example. 2.Sometimes, I like to remind my students that the examples I have for them in...

You all. Many people, but mostly Jenny Terry and Andy Fields, but also a number of my Twitter mutuals,  collected a crap ton of data from statistics students worldwide .  See: Here is the article describing the project . The data is embargoed until October 2024, but you can contact the corresponding authors if you would like early access. Also, they have tons and tons of documentation available at OSF . So you can come up with your own hypotheses and test them. Which is very, very generous.

  The unstoppable pop of Taylor Swift (reuters.com) Here are some ideas for using this to teach statistics: Data visualizations and visualization guides: With cats, y'all. And the Taylor Swift handwriting font. I love the whole vibe of this as well as how they explain their data visualizations. Operationalizing things: The page describes three Spotify metrics for music: Acousticness, danceability, and emotion. The data visualization contains a numeric value for each metric and a description of the metric's meaning. DATA!: Okay. This is an excellent example of things already. And it is delightful. Then I thought, "Oh, wouldn't it be fun if this was in spreadsheet form!" (I think that A LOT, friends). But, as I write a book and my syllabi, I don't have time for that,  BUT A REDDITOR DID HAVE TIME FOR THAT . Dr. Doon created a spreadsheet with 18 columns of Spotify data for each son. It doesn't include the Midnights data but is still a fantastic amount of dat...

Consumer Affairs released a list of America's best and worst drivers . It is a short article but contains many good stats nuggets. 1. Ratio and ordinal versions of the same data. 2. Where did the ratio data come from? Take a look at the Methodology. 3.  Here is the data for the twenty most terrible driver s. It includes the nominal/ratio data I shared above and the top four bullet points from the image above. 4. Where did they find their data? Lucky for us, they cite their data. Which is good form, right? But also, it is an example of how much hecking data is out there. 

I'm a fan of former NASA engineer and current YouTuber/science comm pro  Mark Rober . He meets the sweet spot of containing YouTube content that is safe for kids but also engaging for adults. You may know him for creating obstacle courses for squirrels in his backyard and holding the world record for the tallest elephant toothpaste explosion .  Recently, I discovered that he made a stats-adjacent video  explaining machine learning by studying baseball signals and creating a way to de-code baseball signals . Anyway, if you touch on your topics in your classes, this is a great, quick explainer. It is well-edited, well-produced, and has captioning. You don't need to be a baseball fan to follow this example. 

 I have written about the NFL's response to concussion data as a case study of how to obfuscate data. This has been covered in many places, including in The Atlantic and on PBS . In my experience, concussions are a prime source of conversation for traditionally college-aged students. Many of them were high school athletes. Fewer are college athletes. Most college students have personally experienced a concussion or loves someone who has. Now, the University of Pittsburgh is opening the National Sports Brain Bank . This is for athletes, not just football players. Two former Steelers have promised their brains, as have two scientists who played contact sports.  Here is a press release from the University of Pittsburgh . Here is a news report  featuring the two Steelers who have promised to donate their brains. However, as described by Aschwander, we still don't know how many football players have CTE (please read this piece, it is such good stats literacy from Aschwander...

Looking to freshen up your readings for Research Methods? Or for a good, brief RM primer for a stats or psych class? Check out Our World in Data's "Why randomized control trials matter and the procedures that strengthen them" . Added bonus: Our World in Data dived into their data archives to illustrate each piece with their own research. I don't know about you, but my brain far prefers abstract concepts paired with concrete examples.  Some of the classic include: -Why we need RCT. https://ourworldindata.org/randomized-controlled-trials#what-are-randomized-controlled-trials -Why causal inference is hard. https://ourworldindata.org/randomized-controlled-trials#the-fundamental-problem-of-causal-inference -Why we need control groups. https://ourworldindata.org/randomized-controlled-trials#the-control-group-gives-us-a-comparison-to-see-what-would-have-happened-otherwise

 https://www.npr.org/sections/goatsandsoda/2023/05/10/1175303067/a-plastic-sheet-with-a-pouch-could-be-a-game-changer-for-maternal-mortality https://www.bmj.com/content/381/bmj.p1055 I love this study, in and of itself, because it is based on research that will save women's lives without spending a lot of money. I love it.  Here is a link to the original study . I learned about it from an NPR story about the research by Rhitu Chaterjee . I also love it because it is an accessible example of a bunch of statistics things: Dependent variables...operationalizing variables...why cross-cultural research and solutions aren't just lip service to diversity...how control groups in medical research are very different than control groups in psychology research...absolute vs. relative risk. -Dependent variables/operationalized variables: This study clearly illustrates the power of measurement and operationalization. The researchers wanted to create a way to better assess post-childbirth h...

It shouldn't come as a shock that the CDC shares data on rates of public health issues in the US.  However, you may be unaware of the available data and interactive visualizations provided by the CDC and the different ways you can use them in class . 1. Teach your students a lesson about good sources for mental health data. 2. Show your students how data visualizations can help present and simplify complex data. https://www.cdc.gov/nchs/covid19/pulse/mental-health.htm 3. Get into the research methods. Everyone has heard of the census, but fewer have heard of the Household Pulse Survey (https://www.census.gov/data/experimental-data-products/household-pulse-survey.html). The US Census collects much information between the 10-year census, including mental health data. https://www.census.gov/data/experimental-data-products/household-pulse-survey.html 4. Talk about how the government assesses depression and anxiety. For example, you can show how the basic methodology uses a valid, relia...

I think it is important to emphasize how regression can be used to make future predictions using trends in existing data. Most psychology books use psychology examples to illustrate this, which makes sense. Still, I think explaining how regression is widely used in business to make financial decisions, and predictions is important. But that can be boring. But I found one example that uses the Marvel Comic Universe to do this. I already blogged about this , but I'm sharing exactly how I used this in class presently. ASIDE: This data is being regularly updated! Here is a Google Drive folder with 1) my version of the data (CSV and I turned all the percentages to decimal points for JASP) and 2) my PPT . Which includes photos of the scientists of the MCU. ALSO: While your students are doing their exercise, totes play the soundtrack from Guardians of the Galaxy. Do it. 

File under: End of the semester stress blogging about a person who brings me joy. Taylor Swift (see: sampling error with Taylor ). Here is a new, VERY accessible example of ordinal data . Rob Sheffield, writing for Rolling Stone, rank-ordered ALL of Dr. Swift's songs.  https://www.rollingstone.com/music/music-lists/taylor-swift-songs-ranked-rob-sheffield-201800/bad-blood-2014-196114/ Also, introduce your students to Methods Section 😁. This rank order is based on the variable "Taylor genius". You could even use this as an example of anti-interrater reliability. This ranking comes from exactly one person. AND YOU'RE ON YOUR OWN KID DESERVED BETTER. Each ranking includes the best lyric from the song as well as a brief description of the Taylor Genius on display. Is this also an example of qualitative data? https://www.rollingstone.com/music/music-lists/taylor-swift-songs-ranked-rob-sheffield-201800/the-great-war-2022-1234617639/

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...

TL;DR: One way to avoid government regulation is by simply refusing to share data that may lead to government regulation (and safer trains). I'm looking at you, railroads. _____________________________________________________________________________ Not every example I post syncs directly to the typical Psychological Statistics curriculum. I also post about statistical literacy. Like why data matters and counts. And how very, very simple data could help illuminate and solve real-world problems, but only if we can access that data. I get good and mad at organizations that avoid responsibility by manipulating and/or withholding data.  See: Organizations that  share data but in a functionally inaccessible way. Also, I created a spreadsheet (of course I did) containing several examples of times when large organizations goofed around with data so they wouldn't get sued. It looks like I should add rail roads to this list. Aside: I grew up not 10 miles from the world-famous Horsesho...

Friends. You don't know what finding fun stats blog content has been like over the last few years. All of the data writers/websites I followed were always writing about, explaining, and visualizing COVID or political data (rightfully so). I prefer examples about puppies , lists of songs banned from wedding reception s, and ghosts . Memorable examples stick in my students' heads and don't presuppose any knowledge about psychological theory.  Due to the lack of silly data and my own life as a professor, mom of two, wife, and friend, my number of posts during The Rona definitely dipped.  But now, as the crocuses bloom in Erie, PA, the earth, and I, are finding new life and new examples. Nathaniel Rakich, writing for FiveThirtyEight, wrote a whole piece  USING TAYLOR SWIFT TO EXPLAIN POLLING/SAMPLING ERROR S. Specifically, this article tackles three different polling firms and how they went about asking Americans which Taylor Swift album is their favorite Taylor Swift album....