This website is associated with Agresti, Franklin, and Klinenberg's text Statistics, The Art and Science of Learning from Data ( @artofstat ), and there are dozens of great interactives to share with your statistics students. Similar and useful interactives exist elsewhere, but it is nice to have such a thorough, one-stop-shop of great visuals. Below, I have included screengrabs of two of their interactive tools. They also explain chi-square distributions, central limit theorem, exploratory data analysis, multivariate relationships, etc. This interactive about linear regression let's you put in your own dots in the scatter plot, and returns descriptive data and the regression line, https://istats.shinyapps.io/ExploreLinReg/.  Show the difference between two populations (of your own creation), https://istats.shinyapps.io/2sample_mean/

Your students have heard of probiotics. In pill form, in yogurt, and if you are a psychology major, there is even rumbling that probitotics and gut health are linked to mental health. But this is still an emerging area of research. And NPR did a news story about a clinical trial that seeks to understand how probiotics may or may not help eliminate GI problems in children who are on antibiotics . Ask any parent, and they can tell you how antibiotics, which are wonderful, can mess with a kid's belly. When they are already sick. Science is trying to provide some insight into the health benefits of probiotics in this specific situation. They spell out the methodology: How to use in class: 1) I love about this example is that the research is happening now, and very officially as an FDA   clinical trial . So talk to your students about clinical trials, which I think you can then related back to why it is good to pre-register your non-FDA research, with explicit research m...

So, I am very pro-science, but I have a soft spot in my heart for medical research that improves medical outcomes without actually requiring medicine, expensive interventions, etc. And after spending a week in the NICU with my youngest, I'm doubly fond of a way of helping the littlest and most vulnerable among us. One example of such was published in the journal Pediatrics and written up by NPR . In this case, they found that fewer mistakes are made when not-yet-named NICU babies are given more distinct rather than less distinct temporary names. The unnamed baby issues is an issue in the NICU, as babies can be born very early or under challenging circumstances, and the babies' parents aren't ready to name their kids yet. Traditionally, hospitals would use the naming convention "BabyBoy Hartnett" but several started using "JessicasBoy Hartnett" as part of this intervention. So, distinct first and last names instead of just last names. They measured patie...

How much do our personalities change when we're drunk? Not as much as we think. We know this due to the self-sacrificing research participants who went to a lab, filled out some scales, got drunk with their friends. For science! Here is the research, as summarized by the first author .  Here  is the original study. This example admittedly panders to undergraduates. But I also think it is an example that will stick in their heads. It provides good examples of: 1) Self-report vs. other-report personality data in research. -Two weeks prior to the drinking portion, participants completed a Big Five personality scale as if they were drunk. So, there is the self-report of Drunk!Participant. And during the drinking session, participants had their Big Five judged by research assistants coding their interactions with friends, allowing a more object judgment of the Drunk!Participant. The findings: https://www.psychologicalscience.org/news/releases/personality-may-change-whe...

This is an excellent video for introducing t -tests AND finally getting the story straight regarding William Gossett, Guinness Brewery, and why Gossett published under the famous Student pseudonym. What did I learn? Apparently, Gossett DID have Guinness' blessings to publish. Also, this story demonstrates statisticians working in Quality Assurance as the original t-tests were designed to determine the consistency in the hops used in the brewing process. Those jobs are still available in industry today. Credit goes to the RStats Institute at Missouri State University.  This group has created many other tutorial videos for statistics as well.

UPDATE: 9/22: Sex ratio in India is normalizing: https://www.pewresearch.org/religion/2022/08/23/indias-sex-ratio-at-birth-begins-to-normalize/ I use this story from The Economist as a conceptual explanation of the one-sample t-test.  TL:DR: Sex ratio disparity data out of India is an abstract introduction to the one-sample t -test. So, at its most basic, one sample t -test uses some given, presumably true number/mu and tests your sample against that number. This conceptual example illustrates this via the naturally occurring sex ratio in humans (your mu) versus 2006-8 sex ratio data from different states in India (your sample data). Why look at this data? Social pressure, like dowries, high rates of sexual violence against women in India, etc., make male offspring more attractive than female offspring to some families. And the data provides evidence that this is leading to disturbing demographic shifts. For example, see the table below from The Economist: http://www.ec...

People don't always tell the whole truth on dating websites, embellishing the truth to make themselves more desirable. This example of how OK Cupid users lie about their heights is a good example for conceptually explaining null hypothesis testing, t -tests, and normal distributions. So, Cheng, article author and data enthusiast, looked through OK Cupid data. In this article, she describes a few different findings, but I'm going to focus on just one of them: She looked at users' reported heights. And she found a funny trend. Both men and women seem to report that they are taller than they actually are. How do we know this? Well, the CDC collects information on human heights so we have a pretty good idea of what average heights are for men and women in the US. And then the author compared the normal curve representing human height to the reported height data from OK Cupid Users. See below... From http://nycdatascience.com/okcupid-scraper/, by Fangzhou Cheng  ...

Tis the season for the end of semester teaching evaluations. And Ben Schmidt has created an interactive tool that demonstrates gender differences in these evaluations.  Enter in a word, and Schmidt's tool returns to you how frequently the word is used in Rate Your Professor  evaluations, divided up by gender and academic discipline. Spoiler alert: Men get higher ratings for most positive attributes! ...while women get higher ratings for negative attributes.  Out of class, you can use this example to feel sad, especially if you are a female professor and up for tenure. In class, this leads to obvious discussions about gender and perception and interpersonal judgments. You can also use it to discuss why the x- and y-axes were chosen. You can discuss the archival data analysis used to generate these charts. You can discuss data mining. You can discuss content analysis. You can also discuss between-group differences (gender) versus within-group differences (acade...

Once again, Kristopher Magnusson has combined is computer programming and statistical knowledge to help illustrate statistical concepts . His latest  interactive tool allows students to view the t-curve for different degrees for freedom. Additionally, students can view error rates associated with different degrees of freedom. Note that the critical region is one-tailed with alpha set at .05. If you cursor around the critical region, you can set the alpha to .025 to better illustrate a two-tailed test (in terms of the critical region at which we declare significance).  Error rates when n < 30 Error rates when n > 30 This isn't the first time Kristopher's interactive tools have been featured on this blog! He has also created websites dedicated to explaining effect size , correlation , and other statistical concepts .

I take credit for none of these. A few have been posted here before. by Rayomond Biesinger, http://fifteen.ca/ Creator unknown, usually attributed to clipart? http://www.sciencemag.org/content/331/6018.cover-expansion https://www.flickr.com/photos/lendingmemo/ https://lovestats.wordpress.com/2014/11/10/why-do-kids-and-you-need-to-learn-statistics-mrx/ http://memecollection.net/dmx-statistics/ 9/23/15 Psychometrics: Interval scale with proper anchors 2/9/16 4/19/16 4/28/16 "Symbols that math urgently needs to adopt" https://mathwithbaddrawings.com/2016/04/27/symbols-that-math-urgently-needs-to-adopt/ http://www.mrlovenstein.com/ http://www.smbc-comics.com/ 9/8/16 2/9/2107 https://hbr.org/2017/02/if-you-want-to-motivate-employees-stop-trusting-your-instincts https://www.theguardian.com/politics/2017/jan/19/crisis-of-statistics-big-data-democracy?CMP=share_btn_tw 2/13/17 ...