Time to make this video of Dr. Fauci testifying before Congress Not Awful and Boring Cannon. Because it is beautiful to behold. Here, Dr. Fauci drops truth bombs at a congressional hearing about COVID-19 research. In it, he critiques a hydroxychloroquine research study for not having a control group, having confounds, no randomization, and he talks smack about peer review. And the most important thing? He states that he would change his mind about hydroxychloroquine if compelling data from a well-designed study indicated that he should do so. Because science changes when the evidence changes.  

The Economist created interactive visualizations for various polling data related to the 2020 U.S. Presidential election.  While illustrating this data, they used different measures of central tendency and different confidence intervals. Like, it is one thing to say that Candidate A is polling at 47% with a margin of error of 3.2%. I think it is much more useful to illustrate what the CI is telling us about the likely true parameter, based on what we have collected from our imperfect sample. The overlap in confidence intervals when polling is essential to understanding polling.  How to use in class: 1) Electoral college predictions, illustrated with median, 60%, and 95% confidence intervals. Also, I like how this illustrates the trade-off between precision and the size of a confidence interval. The 60% CI is more narrow, but you are only 60% confident that it contains the true number of electoral college votes. Meanwhile, the 95% confidence interval is much wide but also more ...

This is an engaging example of mode.  Barbara Thompson, writing for NetCredit, shared a report on the most common last names in every country, beautifully summarized via poster . The color coding represents the origins of the last names. I stared at this map for a very long time when I first saw it. NetCredit also shared its data via Google Sheet . How to use in class:  1) Mode example. 2) Ask your students how they think this data was calculated, then send them to the webpage to learn more about how the data was calculated.  3) Go to the full article for this data. They break the data up, continent by continent, to explain how the modal names came to be due to naming tradition/history in each country/region/continent. I'm a big nerd and think that sort of thing is fascinating. 

This is a beautiful story from Reddit, with a very kind Redditor, Higgnenbottoms/Quoc Tran, who shared his data with all of us, so we can use this as an example of a) independent t-tests, b) violin plots, AND R.  So, user r/quoctran98  wanted to know if Daves knew more Daves than non-Daves do. HA! He started by collecting data from r/samplesize .  Do you all know about that subreddit, where you can post a survey and see who responds? You're welcome. Anyway, Quoc analyzed his data AND created a violin plot to illustrate his data. He shared it at r/dataisbeautiful , which is another excellent stats subreddit. See below. AND...here is the kicker...I contacted Quoc, and he shared his data (so your students can run their t-tests) AND his R code . I cleaned up his data a bit to provide the same results as the graph above (he had someone report that they knew 69 Daves. I mean, he collected the data from Reddit users.).

"The answer is always more spreadsheets." -Michael Hobbes The good news: 1) This isn't a COVID example. 2) This is one of those examples that gets your students to think more abstractly about some of the tougher, fundamental questions in statistics. Precisely: How do we count things in the very, very messy real world? What are the ramifications of miscounting messy things? 3) The example comes in the form of the very engaging podcast You're Wrong About , hosted by Michael Hobbes and Sarah Marshall. @yourewrongabout The bad news: The example is about human trafficking, so not nearly as fluffy as my hotdog or seagull posts. That said, this episode of the You're Wrong About podcast, or even just the first 10 minutes of the episode, reveals how hard it can be to count and operationalize a variable that seems pretty clear cut: The number of children who are trafficked in America every year.  The You're Wrong About podcast takes misunderstood, widely reported event...

Many stats instructors use dice and/or coin flips to teach their students about distributions, probably, CLT. Here is an alternative to physical coins and dice, in case you are teaching from a distance. Certainly, there are countless other websites that will roll a dice or flip a coin for you, but these simple websites created by Google are intuitive and pretty. Using Google's dice rolling simulator, y ou can roll a standard six-sided die. Or a DnD 20 sided die. Or multiple dice.  I included the link, but all you need to do Google "Roll dice" to get to the website. Google also lets you flip a coin.  This simulator doesn't have any fancy options, and you can get to it simply by Googling, "Flip a coin". 

When we talk false positives in psych stats, it is usually in the context of NHST, which is abstract and tricky to understand, no matter how many normal curves you draw on the dry erase board. We also tend to frame it in really statsy terms, like alpha and beta, which are also tricky and sort of abstract, no matter how many times you repeat .05 .05 .05. In all things statistics, I think that abstract concepts are best understood in the context of real-life problems. I also think that statistics instructors need to emphasize not just statistics but statistical thinking and reasoning in real life. To continue on a theme from my last post, students need to understand that the lessons in psych stats aren't just for performing statistics and getting a good grade, but also for improving general critical thinking and problem-solving in day to day life. I also think that our in-class examples can be too sterile. They may explain Type I/II error accurately, but we tend to only ask our stude...

One of my big pedagogy concerns, as a psychologist who teaches psychology majors, is this: Are we explicitly drawing the links between psychological science and ALL of the other sciences, and the fact that many of the lessons they learn in their psychology classes apply to other sciences?  I think this is an issue in statistics. I always emphasize that I do not simply teach statistics for psychologists: I am teaching them statistics, full stop. I think we also have to emphasize to our majors that the psychology research process is, in many ways, just the broad research process use in science. As such, our lessons aren't just teaching them major-specific content, but we are teaching them information that leaves them better prepared to interpret scientific research they encounter.  This includes a potential ugly part of the research process: Bad science reporting via over-hyped research press releases.  As such, I present this great piece from CNN, "Science by press release...

Physiologist Dr. James Smoliga published an article entitled " Modeling the maximal active consumption rate and its plasticity in humans - perspective storm hot dog eating competitions. " While hot dog eating competitions may not seem germane to Serious Academic Discourse, the idea of stomach/gut plasticity certainly is. Spoiler alert: According to the models, the maximum capacity of a stretched-out human stomach is 84 hot dogs. And buns.  Honestly, all of the GIFs of humans eating hotdogs were nasty, so enjoy this cutie. However, my blog post is about something other than the researcher's findings as much as it is about  Nathan's Hotdog Eating Contest spreadsheet that Smoliga created while performing his research. A spreadsheet packed with 16 variables and 430 hot dog eating participants your students can analyze in Stats class. I'm surprised that Nathan's didn't have its own database. Here is a description of how they generated the database.  Independent...

This NPR story by Richard Harris describes science's struggle to keep up with the massive amount of COVID-19 research, much of which is coming out of China. How does science, and society, judge the quality of these papers?   How to use in class: 1.  How do scientists assess the quality of research? By reading pre-registered reports and pre-prints : The report explains pre-prints and pre-registration! The good: The research gets out faster. Reviewers can compare pre-planned analysis to the actual analysis. The bad: The media gets too excited about pre-prints. The report describes the totally overwhelming number of pre-prints for COVID-19 related research: One of the scientists interviewed in the piece describes how he used pre-registered information to assess a COVID-19 research paper: 2. How do scientists assess the quality of an article: By the author and their academic affiliation. The report describes the bias that may exist when we lean on author/affiliation heuristics i...

Niv Elis, writing for The Hill, summarized a report created by JP Morgan analyst Jesse Edgerton. The report found a link between in-restaurant spending from three weeks ago and increases in new cases of COVID-19 in different states now. Data for the analysis came from 1) J.P. Morgan/Chase in-restaurant (not online/takeout) credit card purchases and 2) infection data from Johns Hopkins.  How to use in class: 1. Correlation/regression: This graph, which summarizes the main findings from the report, may not include my beloved APA axis labels, but it does include an R2 and is a good example of a scatterplot.  ALSO: The author of The Hill piece was careful to include this information from the study's author, which clarifies that correlation doesn't necessarily equal causation. 2) Creativity in data analysis: Often, in intro psych stats, we use examples rooted in traditional social science research. We should use such an example. But we MUST also use examples that demonstrate how d...

I started this blog with the hope of making life easier for my fellow stats instructors. I share examples and ideas that I use in my own classes in hopes that some other stats instructor out there might be able to incorporate these ideas into their classes. As we crash-landed into the online transition last Spring, I created took some of the blog posts and made them into lengthier class lessons, including Google Slides and, when applicable, data sets shared via my Google Drive. I ended up with four good lessons about the four big inferential tests typically cover in Psych Stats/Intro Stats: T-test, ANOVA, chi-square, and regression. I think these examples serve as great reviews/homework assignments/an extra example for your students as they try to wrap their brain around statistical thinking. As we are preparing for the Fall, and whatever the Fall brings, I wanted to re-share all of those examples in one spot. Love, Jess ANOVA https://notawfulandboring.blogspot.com/2020/04/online-day-6...

People I love very much live in Florida. My very favorite academic conference is held in Florida. I want Florida to flatten the curve. But Florida is flattening the curve. Believe me when I say that I'm not trying to dunk on Florida, but Florida has provided me with prime material for statistics teaching. Timely material that illustrates weaponized data. Some examples are more straightforward, like median and poor data visualization. Others illustrate a theme that I cover in my own stats class, a theme that we should all be discussing in our stats class: Data must be very, very powerful if so many large organizations work so hard to discredit it, manipulate it, and fire people who won't. You should also point out to your students that organizations working so hard to discredit are typically straightforward descriptive data, not graduate-level data analysis.  1. Measures of Central Tendency As of June 23, the median age of people newly diagnosed with COVID-19 in Florida dropped ...