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...