When I teach inferential statistics, I think it is helpful in providing several conceptual (no by hand calculations, no data analyzed via computer) examples of experiments that could be analyzed using each inferential test. I also think it is essential to use non-psychology examples and psychology examples because students need to see how stats apply outside of psychology. At times, I believe that students are convinced that a class called Psychological Statistics doesn't apply outside of psychology.  So I like this quick, easy-to-follow example from medicine. Thomas, Patel, and Bittel (2021) studied how different vitamin supplements affected outcomes for people with COVID-19. The factor (COVID intervention) has four levels (usual care/control, ascorbic acid, zinc gluconate, and ascorbic acid/zinc gluconate). And the four groups acted pretty much the same. Bonus stats content: Error bars, super-cool Visual Summary of a research study that really highlights the most essential parts...

This brief video from NPR (they make videos, what?) describes how the CDC will be randomly sampling Atlanta residents to test for COVID-19 antibodies. The efforts hope to provide a better estimate of the spread of the disease. H/t to Sy Islam for sharing this with me. I think you could use this in class as a super-fast example of how we use samples to generalize about larger populations. The CDC is sending out employees to conduct antibody tests on a random sample of Atlanta residents. The tests are meant to show how many people have been infected with the coronavirus. pic.twitter.com/mXqznHUJmV — NPR (@NPR) April 29, 2020

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...

As a research-parent-nerd joke before Christmas, six doctors swallowed Lego heads and recorded how long it took for the Lego heads to pass. 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-...

As described in Wired's pop piece, the average body temperature for healthy adults isn't 98.6℉. Instead, data suggests that it is 97.7℉. Here is a link to the original study by  Hausmann, Berna, Ggujral, Ayubi, Howekins, Brownstein, & Dedeoglu . 1. This is an excellent theoretical example for explaining a situation where a one-sample t-test could answer your research question. 2. I created fake data that jive with the results, so you can conduct the test with your students. This data set mimicked the original findings for healthy adults (M = 97.7, SD = .72) and was generated with Andy Luttrell's Data Generator for Teaching Statistics . 97.39 97.45 97.96 97.35 96.74 99.66 98.21 99.02 96.78 97.70 96.90 97.29 97.99 97.73 98.18 97.78 97.17 97.34 97.56 98.13 97.77 97.07 97.13 9...

Patti Neighmond, reporting for NPR, wrote a piece on how the medical establishment's method for assessing patient pain is evolving . This is a good example of why it can be so tricky to operationalize the abstract. Here, the abstract notion in pain. And the story discusses shortcomings of the traditional numeric, Wong-Baker pain scale, as well as alternatives or complements to the pain scale. No one is vilifying the scale, but recent research suggests that what a patient reports and how a medical professional interprets that report are not necessarily the same thing. From Dr. John Markman's unpublished research: I think this could also be a good example of testing for construct validity. The researcher asked if the pain was tolerable and found out that their numerical scale was NOT detecting intolerable. This is a psychometric issue. One of the recommendations for better operationalization: Asking a patient how pain effects their ability to perform every day tas...

Writing for the Dallas News, Jackie Wang describes how data entry errors lead to the erroneous belief that Texas pregnancy related death rates were more than double the national rate. Short story: Texas thought it had terribly high rates of pregnancy related deaths. It didn't. Turns out that folks were just using the online system for reporting cause of death incorrectly. So, human data entry errors lead to what looked like a spike in maternal deaths. Like, whenever I make a change in my grade book columns in Blackboard, I always forget to hit "Save" and then the changes I make aren't saved. Only here, that sort of small error caused Texas to think that death rates for pregnancy complications was 14.6:100,000, not the reported 38.4:100,000. Which is an enormous difference. And a lot of money was spent to rectify the problem, which wasn't a problem, but those actions were probably still good for women and babies and families. This article details how Texas had ...

You know what, I've been doing this blog for YEARS and I don't have a single example of moderation. Until now. This CNN story summarizes brand new research findings that indicate that alcohol and/or tobacco use mediate the relationship between drinking really hot tea and developing esophageal cancer. So, the really hot tea-cancer relationship does not exist in the absence of smoking and/or alcohol consumption, but it is there if you do indulge in either smoking or alcohol consumption. And writing this post reminded me of this Arrested Development moment: Aside: -This article could also be a good example of the need for cross cultural research: Americans don't love tea as much as other parts of the world do. And, super hot tea (145 degrees +) is very popular outside of the US and Europe. The present research was conducted in China.

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...

Factorial ANOVA example ahead! With a lovely interaction. And I have a year old and a 4.5 year old and they are sickly daycare kids, so this example really spoke to me. NPR did a story about a recent publication that studied how we administer medicine to our kids and provides evidence for a few things I've suspected: Measuring cups for kid medicine are a disaster AND syringes allow for more accurate dosing, especially if the dose is small. The researchers wanted to know if parents properly dosed liquid medicine for their kids. The researchers used a 3 (dosage, 2.5, 5.0, 7.5 ml) x 3 (modality: small syringe, big syringe, medicine cup) design. They didn't use factorial ANOVA in their analysis, this example can still be used to conceptually explain factorial ANOVA. Their findings: How to use in class: -An easy-to-follow conceptual example of factorial ANOVA (again, they didn't use that analysis in the original paper, but the table above illustrates factorial ANO...

C ollin gleaned some archival data about ER visits in America from US Consumer Product Safety Commission. For each ER visit, there is a brief description of the reason for the visit. Collin queried punching related injuries. See his Method section below, which describes how he set the parameters for his operationalized variable. With a bit of explaining, you could also describe how Collin took qualitative data (the written description of the injury) and converted it into quantitative data: http://qz.com/582720/americas-most-prolific-wall-punchers-charted/ Then he made some charts. The age of wall punchers is right-skewed. And probably could be used in a Developmental Psychology class to illustrate poor judgment in adolescents as well as the emergence of the prefrontal cortex/executive thinking skills in one's early 20s. http://qz.com/582720/americas-most-prolific-wall-punchers-charted/ The author looked at wall punching by month of the year and uncovered a fairly un...

I just used this example in class, and I realized that I never shared it on my blog. I really love this example of Type II error (and some other stuff, too). So here it goes. http://www.achievement.org/autodoc/page/mar1int-1

My son, Artie, resting in the NICU When my second son was born via emergency c-section, he spent a week in the NICU out of an abundance of caution. It wasn't fun, but Artie pulled through just fine. He is a fat, happy four-month-old now. While we were there, I found out that many of the other NICU babies there were suffering from neonatal abstinence syndrome (NAS). They were born addicted to drugs. And those poor babies howled for hours as they were being weaned off of drugs and helped by staff. NPR's All Things Considered recently did a series about national efforts to help end NAS. Two of the segments from this series are possible learning moments for statistics and RM classes. One discusses efforts to use proper research methodology to create better treatment recommendations for NAS babies . The second discusses governmental efforts to use systematic data collection to better track NAS babies and get to the root of the problem . 1. Using clinical research to bette...

This story describes an 1) an efficacy study that 2) touches on some I/O/Health psychology research and 3) has gained the unwanted attention of government regulatory agencies charged with protecting research participants.   The study described in this story is an efficacy study that questions a decision made by the 2003 Accreditation Council for Graduate Medical Education. Specifically, this decision capped the number of hours that first-year medical student can work at 80/week and a maximum shift of 16 hours. The PIs want to test whether or not these limits improve resident performance and patient safety. They are doing so by assigning medical students to either 16-hour maximum shifts or 30-hour maximum shifts. However, the research participants didn't have the option to opt out of this research. Hence, an investigation by the federal government. So, this is interesting and relevant to the teaching of statistics, research methods, I/O, and health psychology for a numbe...

Statistics aficionados over at FiveThirtyEight applied statistics (specifically, tools used by epidemiologists) to the Summer of 2015  outbreak of Legionnaires' Disease  in New York. This story can be specifically used in class as a way of discussing how simple bar graphs can be modified as to display important information about the spread of disease. This news story also includes a link to a learning module  from the CDC. It takes the user through the process of creating an Epi curve. Slides 1-8 describe the creation of the curve, and slides 9-14 ask questions and provide interactive feedback that reinforces the lesson about creating Epi curves. Graphs are useful for conveying data, but even one of our out staples, the bar graph, can be specialized as they share information about the way that disease spread. 1) Demonstrates statistics being used in a field that isn't explicitly statistics-y. 2) A little course online via the CDC for your students to learn to...

This story describes a 1966 journal article that argues that signing an informed consent isn't the same as truly giving informed consent. I think this is a good example for the ethics section of a research methods class as it demonstrates some deeply unethical situations in which participants weren't able to give informed consent (prisoners, non-English speakers, etc.). Indeed, the context within which the informed consent is provided is very important. It also provides a historical context regarding the creation of Institutional Review Boards. The original 1966 article is here .