Xkcd is a gift to Statisitcs instructors . Author Randall Monroe shares his humor and statistics knowledge. I think that many of his comics can be used as extra credit points , in that you don't get the joke unless you get the conceptual statistical knowledge behind the joke. NOTE: I have included images here, but you really, really should go to the original comics and cursor over for the messages to view the alternative text. NOTE TWO: This is not a comprehensive list but I will try to update it as Monroe shares more comics. To teach APA formatting: https://xkcd.com/833/ To explain sufficient sample size in research: https://xkcd.com/507/ To explain good statistics manners/how to appropriately ask for stats help: https://m.xkcd.com/2116/ To explain error bars: https://xkcd.com/2110/ T-test and the t-curve: https://xkcd.com/2110/ Linear relationships: https://xkcd.com/605/ The Normal Curve: https://xkcd.com/2118/ Cherry picking, p-...

Two things this week: 1) Open Science Framework can be used to share teaching materials and 2) Dr. Page-Gould shows us how to do just that, and how to do it very well. Most people who would visit this blog have heard of the Open Science Framework. You probably know that it is a popular place to share research projects/data/pre-register your jam/share materials, but did you know that it is also a popular place to share teaching resources? Dr. Page-Gould recently shared her whole stinking upper level Stats/RM class, Treatment of Psychological data . And it is beautiful and good and makes me feel like an entirely inadequate statistics instructor. Like...she shared EVERYTHING and it is beautiful and a great example of how to fold the "new statistics" into undergraduate stats. Lectures, example data, and lab resources (and rubrics for grading her labs) are available. This is an upper level course but it covers topics that should be included in Introduction to Statistics. I ...

This example provides evidence of data funny business beyond psychology, shows why pre-registration is a good thing, AND uses a chi-square. Bonus points for being couched in medicine and prominently featuring randomized controlled trials (RCT). Basically, Kaplan and Irving's  research checked out the results for RCTs funded with grants from the National Heart, Lung and Blood Institute. See below for how they selected their studies: And what did they find? When folks started registering their outcomes, folks started to get fewer "beneficial" results. Which probably REALLY means that some of those previous "beneficial" results were not so beneficial, or the result of some data massaging. See below: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0132382           Another reason to love this example: It is a real life chi-square that is easy to understand! I feel like I don't have enough great chi-square examples in my lif...

Y'all. I love collecting examples of awesome cookies and cakes and cupcakes beautified with statistics and data and graphs. Here is my Pinterest Board. My goal is to SOMEDAY have the time and skills to make some of my own. Until then, I will gush over other peoples accomplishments:

Do you know what we need as statistic instructors? A) More chi-square examples that are b) rooted in Taco Bell condiments and c) are null. So here you go, as inspired by this tweet : This data did not achieve statistical significance, X^2 (3, N = 32) = 0.33, p = 0.95. The data suggests that these Taco Bell packets are randomly distributed. If you do this analysis by hand, here is your data: Diablo = 8, Hot = 9, Fire = 7,  Mild = 9. If you do this analysis via software, here is the .csv version of the data , here is the .jasp version of the data , and here is a version of the data you can just copy and paste. Sauce Diablo Diablo Diablo Diablo Diablo Diablo Diablo Diablo Fire Fire Fire Fire Fire Fire Fire Fire Fire Hot Hot Hot Hot Hot Hot Hot Mild Mild Mild Mild Mild Mild Mild ...

Stick with me here. I think this would be a great warm-up activity early in the semester. My boy Ace had a research hypothesis, operationalized his research, tried to collect data points using several test subjects, and measured his outcomes. Here is the original interview from  Draft Diamonds  and  Newsweek's story . 1) How did he operationalize his hypothesis? What was his IV? DV? 2) Did he use proper APA headers? Should APA style require the publication of pictures of crying researchers if their findings don't replicate? 3) This data could be analyzed using a repeated measure ANOVA. He had various members of his family throw a football as different PSIs and he measured how far the ball traveled and calculated mean for three attempts at each PSI. 4) His only participants were his mom, dad, and sister. So, this study is probably underpowered. 5) In this video from NBC news , Ace's dad describes how they came up with the research idea. Ace i...

Someone on Reddit posted this cool picture of a...contraption? I'll go with contraption. Anyway, it automatically generates a chart of the amount of sunlight per day by burning a log. A Twitter follower recognized this as a Campbell-Stokes recorder . This is beautiful art and data visualization from Hood-Glen Park in San Francisco. How to use in class: 1) Make a bunch of really dumb log arithm jokes. 2) A nice introduction to data visualization. Maybe this could be paired with more traditional sources of weather data. 3) Also makes me think of other naturally occurring charts: Also, while less pretty, think about all the data that is automatically created every time Google Maps identifies your location (and then warns everyone using Google Maps to avoid traffic slowdowns) or Netflix provides you with recommendations based on viewing habits. The Campbell-Stokes recorder could serve as a metaphorical segue into a discussion about all the automated data collectio...

Here is a YouTube video that summarizes some research findings . The researchers looked at Tweets in order to study how are focus and emotions change with our sleep/wake cycles. And the findings are interesting and not terribly surprising. Folks are mellow and rational in the morning and contemplate their mortality at 2 AM. Make money, get paid. And THIS is why I go to bed by 9 AM. I don't need to think about death at 2:20 AM. How to use in class: 1) Archival data (via Tweet) to explore human emotion. 2) What are the shortcomings of this sample method. To be sure, their data set is ENORMOUS, but how are Twitter users different from other people? Do your students think these findings would hold for people who work the night shift? 3) Go back to the original paper and look more closely at the findings: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197002 4) This data represents one of the ways that researchers collect real-time information ...

This story by Christine Aschwanden  from 538.com  describes the limitations of a JAMA article.   That JAMA article describes a research project that found signs of Chronic Traumatic Encephalopathy (CTE) in 110 out of 111 brains of former football players. How to use in stats and research methods: 1) It is research, y'all. 2) One of the big limitations of this paper comes from sampling. 3) The 538 article includes a number of thought experiments that grapple with the sampling distribution for all possible football players. 4) Possible measurement errors in CTE detection. 5) Discussion of replication using a longitudinal design and a control group. The research: The JAMA article details a study of 111 brains donated by the families deceased football players. They found evidence of CTE in 110 of the brains. Which sounds terrifying if you are a current football player, right? But does this actually mean that 110 out of 111 football players will develop CTE...

Nothing freaks out your students faster than a formula, right? Karly over at The Novice Professor shares some worksheets she created for her students to step them through a few of the most common Intro Stats formulas: standard deviation, z-scores, and correlation.  http://www.thenoviceprofessor.com/blog/teaching-statistical-methods-mostly-formula-free Reasons to use in class: 1) Statistics has its own anxiety scale. I think a lot of that anxiety comes from the math part of a stats scale. These hand outs allow you to introduce the math and formulas without ever using the math and formulas. 2) I am a big fan of introducing statistics conceptually then getting into the nitty gritty of calculation, interpretation of output, etc. I like the formula-free approach here in order to introduce the idea of what frequently used stats, like SD, are really doing.

This interactive website from the BBC will match your student, using their height, gender, and weight, to their Rio Olympic body match. You enter your height, weight, age, and select your gender. It matches you with the athlete who is the most like you. It also provides good examples for distribution, and where you fall on the distribution, for Olympic athletes. I think it also gets students thinking about regression models. After you enter your data, the page returns information about where you fall on the distribution histogram for Olympic athletes by height, weight, and age for your gender. Then, the website returns your topic matches: How to use in class: 1) What other IVs could you collect to determine best sport match (DV)? Family income (I had access to soccer growing up, but not dressage horses)? Average temperature of hometown (My high school had a skiing club but not a beach volleyball club)? This gets your students thinking about multiple regression ...

As a research-parent-nerd joke before Christmas, six doctors swallowed Lego heads and recorded how long it took to pass the Lego heads. Why? As to inform parents about the lack of danger associated with your kid swallowing a tiny toy.  I encourage you to use it as a class example because it is short, it describes its research methodology very clearly, using a within-subject design, has a couple of means, standard deviations, and even a correlation. TL;DR: https://dontforgetthebubbles.com/dont-forget-the-lego/ In greater detail: Note the use of a within subject design. They also operationalized their DV via the SHAT (Stool Hardness and Transit) scale. *Yeah. So here is the Bristol Stool Chart  mentioned in the above excerpt. Please don't click on the link if your are eating or have a sensitive stomach. Research outcomes, including mean and standard deviations: An example of a non-significant correlation, with the SHAT score on the y-axi...

Maki Naro created a terrific comic strip detailing the replication, where it came from, where we are, and possible solutions.  You can use it in class to introduce the crisis and solutions. I particularly enjoy the overall tone: Hope is not lost. This is a time of change in statistics and methodology that will ultimately make science better. A few highlights: *History of science, including the very first research journal (and why the pressure to get published has lead to bad science) *Illustration of some statsy ways to bend the truth in science  *References big moments in the Replication Crisis  *Discusses the crisis AND solutions (PLOS, SIPS, COS)