Monday, April 24, 2017

NYT's "You Draw It" series

As I've discussed in this space before, I think that it is just as important to show our students how to use statistics in real life as it is to show our students how to conduct an ANOVA.

The "You Draw It" series from the New York Times provides an interactive, personalized example of using data to prove a point and challenge assumptions. Essentially, this series asks you to predict data trends for various social issues. Then it shows you how the data actually looks. So far, there are three of these features: 1) one that challenges assumptions about Obama's performance as president, 2) one that illustrates the impact of SES on college attendance, and 3) one that illustrates just how bad the opiod crisis has become in our country.

Obama Legacy Data

This "You Draw It" asks you to predict Obama's performance on a number of measures of success. Below, the dotted yellow line represents my estimate of national debt under Obama. The blue line shows true national debt under Obama. Note: With this tool, you trace your trend line on the graph, press a button, and then the actual data pops up, as well as discussion about the actual data.

We can use this data to see how political affiliation influences assumptions about the Obama presidency. This one can be used both ways: Right-leaning users may assume the worse while left-leaning users assume the best.

How Family Income Affects Children's College Chances

This example uses data to touch on a social justice issue: Whether or not a college education is really accessible to everyone. After you enter your estimate and see the real data, the website returns normative data about performance on the task and how you compare to other users. Below, the dotted line represents the actual data, and my guess was the solid line.

I think this would be useful in a class on poverty and as an example of a linear relationship.

Drug Overdose Epidemic

This example would be good for a clinical psychology, addiction, criminal justice, or public health class. It asks the user to guess number of deaths due to car accident deaths, gun deaths, and HIV deaths in the US. Finally, it asks you to estimate deaths due to drug overdoses. Which have sky rocketed in the last 20 years (see below).

Then it contrasts drug overdose deaths with car accidents, guns, and HIV. This example may also be useful for social psychology, as it hints at the availability heuristic.

How to use in class:
1) Non-statisticians using statistics to tell a story.
2) Using clever visualization to tell a story.
3) The interactive piece here really forces you to connect to the data and be proven right or wrong.

Monday, April 17, 2017

Sense about Science USA: Statistics training for journalists

In my Honors Statistics class, we have days devoted to discussing thorny issues surround statistics. One of these days is dedicated to the disconnect between science and science reporting in popular media.

I have blogged about this issue before and use many of these blog posts to guide this discussion: This video by John Oliver is hilarious and touches on p-hacking in addition to more obvious problems in science reporting, this story from NPR demonstrates what happens when a university's PR department does a poor job of interpreting research results. The Chronicle covered this issue, using the example of mis-shared research claiming that smelling farts can cure cancer (a student favorite), and this piece describes a hoax that one "researcher" pulled in order to demonstrate how quickly the media will pick up and disseminate bad-but-pleasing research to the masses.

When my students and I discuss this, we usually try to brain storm about ways to fix this problem. Proposed solutions: Public shaming of bad journalists, better editing of news stories before they are published, a prestigious award system for accurate science writing. And another idea my students usually arrive upon? Better training for journalists.

So, you can imagine how pleased I was to discover that such classes already exist via Sense about Science USA.

Their mission:

They support this mission in a few different ways. They advocate for registering all medical trials conducted on humans. They are training scientists to more effectively communicate their findings to the public. And, apropos of this blog, they are also training journalists to better understand statistics AND offer one-on-one consulting to journalists trying to understand data.

Here is their description of why it is important to better train journalists.

How to use in class:

1) Instead of just showing students the problems associated with poor science writing, let's show them a possible solution as well.
2) Statistics isn't just for statisticians, statistics are for anyone who wants to better understand policy issues, emerging research, and evidence-based practices in their field.
3) Show your students some examples of poor science writing. Have them develop a brief presentation that would address the most common statistical mistakes made by science writers.

Monday, April 10, 2017

Reddit's data_irl subreddit

You guys, there is a new subreddit just for sharing silly stats memes. It is called r/data_irl/.

The origin story is pretty amusing.

I have blogged about the subreddit r/dataisbeautiful previously. It is is pretty self explanatory and the sub has a hard, fast rule about only posting original content or well-cited, serious content. It is a great sub.

But it leaves something to be desired. That something is my deep desire to see stats jokes and memes.

On April Fool's Day this year, they got rid of their strict posting rules for a day and the dataisbeautiful crowd provided lots of hilarious stats jokes, like these two I posted on Twitter:

The response was so strong, because there are so many of people that love stats memes, that a new sub was started, data_irl JUST TO SHARE SILL STATS GRAPHICS. It feels like coming home to my people.