u/zonination's "Got ticked off about skittles posts, so I decided to make a proper analysis for /r/dataisbeautiful [OC]"

The subreddit s/dataisbeautiful was inundated by folks creating color distributions for bags of candy. And because 1) it is Reddit and 2) stats nerds take joy in silly things, candy graphing got out of hand. See below:

https://www.reddit.com/r/dataisbeautiful/comments/5bojxl/oc_the_data_suggests_that_certain_colors_are_not/

https://www.reddit.com/r/dataisbeautiful/comments/5bmo3a/color_distribution_of_one_more_partysized_bag_of/

https://www.reddit.com/r/dataisbeautiful/comments/5cmemr/a_pie_chart_of_mm_colors_from_a_single_500g_bag_oc/

And because it is Reddit, and, to be a fair, statistically unreliable, other posters would claim that this data WASN'T beautiful because it was a small sample size and didn't generalize. One bag of Skittles, they claimed. didn't tell you a lot about the underlying population of Skittles.

Until Redditor zonination came along, bought 35 enormous bags of Skittles, and meticulously documented the color distribution in each bag. He used R. and created multiple data visualizations. See below. Here is the Reddit post, and here is his Imgur gallery with visualizations and a narrative describing his findings. (Y'all, I know Reddit has a bad reputation at times, but the discussion in this posting is hilarious if you are a stats nerd. Check it out.).

He explained his data with a heat map...

http://imgur.com/gallery/uy3MN

And a stacked bar chart, that really illustrates outlier bags 15 and 16. Imagine if you mistakenly tried to generalize from of those bags?

http://imgur.com/gallery/uy3MN

And he presents the increasingly popular Violin plot.

http://imgur.com/gallery/uy3MN

AND...he shared his data and R code with the world.

How to use in class:
-Discuss proper sample sizes required in order to generalize to a population. I think rouge bags 15 and 16 are especially effective at demonstrating sample error.
-Your students understand the concept of Skittles. Therefore, they will be able to understand the nuances of these different kinds of data visualizations.
-Buy your students some Skittles and replicate them.
-Data and code are available to play around with.

Jess Slide Time (added 10/2/18):

Here are some slides I threw together in order to use this as a conceptual example of the sampling distribution of the sample mean:

Ways to use funny meme scales in your stats classes

Have you ever heard of the theory that there are multiple people worldwide thinking about the same novel thing at the same time? It is the multiple discovery hypothesis of invention . Like, multiple great minds around the world were working on calculus at the same time. Well, I think a bunch of super-duper psychology professors were all thinking about scale memes and pedagogy at the same time. Clearly, this is just as impressive as calculus. Who were some of these great minds? 1) Dr. Molly Metz maintains a curated list of hilarious "How you doing?" scales. 2) Dr. Esther Lindenström posted about using these scales as student check-ins. 3) I was working on a blog post about using such scales to teach the basics of variables. So, I decided to create a post about three ways to use these scales in your stats classes: 1) Teaching the basics of variables. 2) Nominal vs. ordinal scales. 3) Daily check-in with your students. 1. Teach your students the basics...

Leo DiCaprio Romantic Age Gap Data: UPDATE

Does anyone else teach correlation and regression together at the end of the semester? Here is a treat for you: Updated data on Leonardo DiCaprio, his age, and his romantic partner's age when they started dating. A few years ago, there was a dust-up when a clever Redditor r/TrustLittleBrother realized that DiCaprio had never dated anyone over 25. I blogged about this when it happened. But the old data was from 2022. Inspired by this sleuthing, I created a wee data set, including up-to-date information on his current relationship with Vittoria Ceretti, so your students can suss out the patterns that exist in this data.

Tyler Vigen's Spurious Correlations

Tyler Vigen has has created a long list of easy-to-paste-into-a-powerpoint graphs that illustrate that correlation does not equal causation. For instance, while per capita consumption of cheese and number of people who die by become tangled in their bed sheets may have a strong relationship (r = 0.947091), no one is saying that cheese consumption leads to bed sheet-related death. Although, you could pose The Third Variable question to your students for some of these relationships). Property of Tyler Vigens, http://i.imgur.com/OfQYQW8.png Vigen has also provided a menu of frequently used variables (deaths by tripping, sunlight by state) to help you look for specific examples. This portion is interactive, as you and your students can generate your own graphs. Below, I generated a graph of marriage rates in Pennsylvania and consumption of high fructose corn syrup. Generated at http://www.tylervigen.com/

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