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....

OK. Lotte van Rijswijk, writing for Resume.io,  used Instagram photos to determine the happiest college in the United States, United Kingdom, and Australia . Here is the Top 20 list for the US. If you go to the website, you can see similar summaries for the UK and Australian data and an interactive table containing all of the data. Here are some ideas for using it in class: 1. This methodology is pretty interesting. She used smiling recognition software and pictures from Instagram to measure happiness. I think this study would pair well with this study about using software to evaluate smiles: https://journals.sagepub.com/doi/10.1177/0956797617734315 https://resume.io/blog/the-happiest-schools-in-the-us-uk-and-australia 2. Ask your student to consider the sampling error that may result from using Instagram data for any research. For example, are photos on Insta representative of human experiences? Is it reasonable to gather a sample of college-aged students using Insta? 3. The ...

Conflict of interest statement: I am slightly shorter than the average American woman. But I'm adorable, so I score low on the Dark Triad?? This blog post started with me giggling at click-bait headlines, but THEN I realized this is one of those rare articles that use data analyses that we teach in Psych Stats. The journey began when I saw this on Twitter: Hilarious, right? Not to be outdone, the NY Post ALSO needed to cover this study:   https://www.google.com/amp/s/nypost.com/2023/02/27/short-people-more-likely-to-be-psychopaths-study/amp/ I'm wheezing. Immediately, this was a great example of clickbait reporting. The research used The Dark Triad as the theoretical underpinning, and The Dark Triad is like what Mindfulness was 10 years ago in psych research. It is just everywhere. BUT...then I realized this is a very easy-to-read study that you could share with advanced UGs, no problem. What does the original research state? https://www.sciencedirect.com/science/article/pii/S...