Waitress Bella is on TikTok . She shares her beach looks and hauls, like plenty of other influencers. Recently, though, shared a series of TikToks that have a home in our statistics and research methods classes.  Bella had a hypothesis. She suspected that certain hairstyles influenced her customers to tip her more. So Bella tested her hypothesis over a series of within-subject, n = 1 experiments at work ( Bella, 2022a , Bella, 2022b , Bella, 2022c ) This isn't a pre-registered paper with open data, but I think this could be a good discussion piece in a research methods or statistics class. I swear that Kate isn't my burner account. If you really, really wanted to test this hypothesis properly, what would that research look like? 1) What external factors influence tips (day of the week, time of day, etc.)? 2) What factors influence reactions to waitstaff (gender, attractiveness, alcohol)? 3) Would you use a within or between research design to study this (different waitstaff wit...

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...

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.

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 ...

Geoff Cumming Geoff Cumming gave a talk at APS 2014 about the " new statistics " (reduced emphasis on p-value, greater emphasis on confidence intervals and effect sizes, for starters). This workshop is now available, online and free, from APS . The three hour talk has been divided into five sections, and each sections comes with a "Table of Contents" to help you quickly navigate all of the information contained in the talk. While some of this talk is too advanced for undergraduates, I think that there are portions, like his explanation of why p-values are so popular, p-hacking, confidence intervals can be nice additions to an Introduction to Statistics class.

This very accessible summary lists the ways people fib with, misrepresent, and overextend data findings. It was written as an attempt to give non-research folk (in particular, law makers), a cheat sheet of things to consider before embracing/rejecting research driven policy and laws. A sound list, covering plenty of statsy topics (p-values, the importance of replication), but what I really like is that they article doesn't criticize the researchers as the source of the problem. It places the onus on each person to properly interpret research findings. This list also emphasizes the importance of data driven change.

This story discusses the many levels of analysis required to get to the bottom of the hypothesis stated in the title of this story. For instance, are cigarettes or the patch better for mom? The baby? If the patch isn't great for either but still better than smoking, what sort of advice should a health care provider give to their patient who is struggling to quit smoking? What about animal model data? I think this story also opens up the conversation about how few medical interventions are tested on pregnant women (understandably so), and, as such,  researchers have to opt for more observational research studies when investigating medical interventions for protected populations.

Two prominent psychology journals are changing their standards for publication in order to address several long-standing debates in statistics (p-values v. effect sizes and point estimates of the mean v. confidence intervals). Here are the details for changes that the Association for Psychological Science is creating for their gold-standard publication, Psychological Science, in order to improve the transparency in data reporting. Some of the big changes include mandatory reporting of effect sizes, confidence intervals, and inclusion of any scales or measures that were non-significant. This might be useful in class when describing why p-values and means are imperfect, the old p-value v. effect size debate, and how one can bend the truth with statistics via research methodology (and glossing over/completely neglecting N.S. findings). These examples are also useful in demonstrating to your students that these issues we discuss in class have real world ramifications and aren't be...

A great, foreboding video  (here is a link to the same video at YouTube in case you hit the paywall) about the actual size and implication of Type II errors in scientific research. This video does a great job of illustrating what p < .05 means in the context of thousands of experiments. Here is an article from The Economist on the same topic. From TheEconomist

Created by Kevin Clay Priceless. More from Kevin Clay  here Aside: I am so, so pleased to now have Snoop Dogg as a label for my blog.

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 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...

Sometimes I think I'm a big nerd. Then I read about an even bigger nerd and I feel better about myself because I'm less nerdy but then worse about myself because I'm not as hard core as I once thought I was. Especially when their nerd focus is on 1)statistics and 2) The Hunger Games.