The Pudding  took a data-driven approach to test a popular hypothesis: Women's pockets are smaller than men's pockets.  Authors Diehem and Thomas sent research assistants to measure the pockets on men's and women's jeans. They even shared supplemental materials, like the exact form the RAs completed. https://pudding.cool/2018/08/pockets/assets/images/MeasurementGuide.pdf And they used fancy coding to figure out the exact dimensions of the jeans. Indeed, even when women are allowed pockets (I'm looking at you, dressmakers!), the pockets are still smaller than they are in men's jeans. They came to the following conclusion: Amen. Anyway, there are a few ways you can use this in the classroom: 1) Look at how they had a hypothesis, and they tested that hypothesis. Reasonably, they used multiple versions of the same kind of pants. If you check out their data, you can see all of the data points they collected about each type of jeans. They even provide supplemental mat...

Katherine Baicker ,  Oeindrila Dube ,  Sendhil Mullainathan ,  Devin Pope,  and  Gus Wezerek created an interactive, data-driven piece for NYT . It provides a new perspective on how we should proceed with re-opening businesses during the COVID-19 pandemic. They argue that we must consider 1) how long people linger in different types of stores, 2) how often they visit these stores, 3) the square footage of the stores, and 4) the amount of human interaction/surface contact associated with how we shop at different stores.  How to use this in class:    1) Show your students how data can inform real-life problems. Or crises, like how to safely re-open stores during COVID-19. 2) Show your students how data can be used in creative ways to solve problems. The present argument uses cellphone location data. 3) Show your students data viz in real life: Here, scatterplots that really improve the #scicomm potential of this piece. 4) Show your students the rese...

I love good graphic design and lists. This guide to spotting bad science embraces both. And many of the science of bad science are statistical in nature, or involve sketchy methods. Honestly, this could be easily turned into a  homework assignment for research evaluation. This comes from the Compound Interest ( @compoundchem ), which has all sorts of beautiful visualizations of chemistry topics, if that is your jam. 

Annie Minoff delves into the sins of ad hoc hypotheses using several examples from evolutionary science (including evolutionary psychology) . I think this is a fun way to introduce this issue in science and explain WHY a hypothesis is important for good research. This article provides three ways of conveying that ad hoc hypotheses are bad science. 1) This video of a speaker lecturing about absurd logic behind ad hoc testing (here, evolutionary explanations for the mid-life "spare tire" that many men struggle with). NOTE: This video is from an annual event at MIT, BAHFest (Bad Ad Hoc Fest) if you want more bad ad hoc hypotheses to share with students. 2) A quiz in which you need to guess which of the ad hoc explanations for an evolutionary finding is the real explanation. 3) A more serious reading to accompany this video is Kerr's HARKing: Hypothesizing after results are known (1998), a comprehensive take down of this practice.

51% of the babies born in the US are male. Why? For a long time, people just assumed that the skew started at conception. Then Steven Orzack decided to test this assumption. He (and colleagues) collected sex data from abortions, miscarriages, live births (30 million records!), fertility clinics (140,00 embryos!), and different fetal screening tests (90,000 medical records!) to really get at the root of the sex skew/conception assumption. And the assumption didn't hold up: The sex ratio is pretty close to 50:50 at conception. Further analysis of the data found that female fetuses are more likely to be lost during pregnancy. Original research article here . Richard Harris' (reporting for NPR) radio story and interview with Orzack here . Use this story in class as a discussion piece about long held (but never empirically supported) assumptions in the sciences and why we need to conduct research in order to test such assumptions. For example: 1) Discuss the weaknesses of previo...

Rustid, a Reddit redditor, performed a research study in order to determine the proportions of peanut butter contained in different types of Reese's Peanut Butter candies. For your perusal, here is the original  reddit thread  (careful about sharing this with students, there is a lot of talk about how the scales Rustid used are popular with drug dealers), photo documentation via  Imgur , and a  Buzzfeed article  about the experiment. Rustid documented the process by which he carefully extracted and measured the peanut butter content of nine different varieties of Reese's peanut butter and chocolate candies. See below for a illustration of how he extracted the peanut butter with an Exact-o knife and used electronic scales for measurements. http://imgur.com/a/wN6PH#SUhYBPx Below is a graph of the various proportions of peanut butter contained within each version of the Reese's Peanut Butter Cup. http://imgur.com/a/wN6PH#SUhYBPx This example...

I think this very important cookie research is appropriate for the Christmas cookie baking season. I also believe that it provides a good example of the scientific method. Arias started out with a baseline cookie recipe (baseline Nestle Toll House Cookie Recipe, which also served as her control group) and modified the recipe in a number of different ways (IVs) in order to study several dependent variables (texture, color, density, etc.). The picture below illustrates the various outcomes per different recipe modifications. For science! http://www.handletheheat.com/the-ultimate-guide-to-chocolate-chip-cookies Also, being true scientist, her original study lead to several follow up studies investigating the effect of different kinds of pans and flours  upon cookie outcomes. http://www.handletheheat.com/the-ultimate-guide-to-chocolate-chip-cookies-part-2 I used this example to introduce hypothesis testing to my students. I had them identify the null and alternative ...

This NPR story by Patti Neighmond is about determining the underlying cause of U.S. obesity epidemic. As the name of the segment states, it seems to come down to food consumption and exercise, but which is the culprit? This is a good example for research methods because it describes methodology for examining both sides of this question. The methodology used also provides good examples of archival data usage.

I am teaching positive psychology for the first time this semester. One way to quickly teach students that this isn't just Happy Psych. 101 is to show them convincing data collected by an international organization (here, the United Nations) that demonstrates the link between positive psychology and the well-being of nations. This data isn't just for a positive psychology class: You could also use it more broadly to demonstrate how research methods have to be adjusted when data is collected internationally (see item 4) and as examples of different kinds of data analysis (as described under item 1). 1) Report on international happiness data from the United Nations . If you look through the data collected, there is a survival analysis related to longevity and affect on page 66. A graphic on page 21 describes factors that account for global variance in happiness levels across countries. There is also a lot of data about mental health care spending in different nations. 2 ...

Here is a basic lesson plan that one could use to teach the hypothesis testing method in a statistics course. I teach in a computer lab but I think it could be modified for a non-lab setting, especially if you use a smart classroom. The lesson involves learning about a company that makes web-based games that improve memory (specifically, I use the efficacy testing the company did to provide evidence that their games do improve memory). Posit Science is a company that makes computer based games that are intended to improve memory. I use material from the company's website when teaching my students about the scientific method. Here is what I do... Property of positscience.com

I have been using Mueller's CROW website for years. It is a favorite teaching resource among my fellow social psychologists , with TONS of well-categorized resources for teaching social psychology. This resource is also useful to statistics/research methods instructors out there as it contains a section dedicated to research design with a sub-section for statistics.

I originally came across this story via io9.com . More information from the source is available here . Essential, these high-end German shoes are made by a company of devoted atheists. They even have their mailing materials branded with "atheist". And they had a problem with their packages being lost in by the USPS. They ran a wee experiment in which they sent out packages that were labeled with the Atheist tape vs. not, and found that the Atheist packages went missing at a statistically higher rate than the non-denominational packages. I think this could be used in the classroom because it is a pretty straight-forward research design, you can challenge your students to question the research design, simply challenge your students to read through the discussion of this article at the atheistberlin website, introduce your students to Milgram's "lost letter" technique and other novel research methods. Edit: 3/9/2020 If you want to delve further into...