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 ...

In our stats classes, we need MANY examples to convey both theories behind and the computation of statistics. These examples should be memorable. Sometimes, they can make our students laugh, and sometimes they can be couched in research. They should always make our students think. In this spirit, I've collected three small examples from the Pew Research Center's  Race and Ethnicity  archive (I hope to update with more examples as time permits). I don't know if any data collection firm is above reproach, but Pew Research is pretty close. They are non-partisan, they share their research methodology, and they ask hard questions about ethnicity and race. If you use these examples in class, I think that it is crucial to present them within context: They illustrate statistical concepts, and they also demonstrate outcomes of racism.   1. "Most Blacks say someone has acted suspicious of them or as if they weren't smart" Lessons: Racism, ANOVA theory: between-group dif...