Monday, May 23, 2016

John Oliver's "Scientific Studies" with discussion quesions

This hilarious video is making the rounds on the Interwebz. Kudos to John Oliver and his writing team for so succinctly and hilariously summarizing many different research problems...why replication is important but not rewarded, how research is presented to the public, how researchers over-reach about their own findings, etc.  I Tweeted about this, but am making it cannon by sharing as a blog post.

Note: This video has some off-color humor (multiple references to bear fellatio) so it is best suited to college aged students.

I will use this in my Online and Honors classes as discussion prompts. Here are some of the prompts I came up with:

1) In your own words, why aren't replications published? How do you think the scientific community could correct this problem? 
2) In your own words, explain just ONE of the ways in which a RESEARCHER can manipulate their own data and/or research findings. It should be one of the methods of manipulation described within the video. Also, don't just name the method of manipulation, explain it like you would explain it to a friend so that they could become aware of the issue AND know how to spot the problem. 
3) Given what you have learned in this video AND your own experiences, who/what do you think is the most to blame for spreading bad science? 
4) Given your response to item 3, describe one way to correct for this problem of misinterpreted data being shared inappropriately. 

5) Why are replications important?

6) What major shortcoming of the "champagne" study was glossed over by the media? What major shortcoming of the "chocolate/pregnancy" study was glossed over? What is the difference between how study authors handle limitations of their work versus how the media handles shortcomings in their work? 

7) What were the red flags from the "hydration" study. Which do you consider to be the most damning and why?

BONUS POINT: Come up with a catchy pick-up line using the spotty Oxytocin research described in the clip.

Additionally, here is another one of my blog post (with links to other posts) related to the topic of scientific reporting.

Monday, May 16, 2016

Cheng's "Okcupid Scraper – Who is pickier? Who is lying? Men or Women?"

People don't always tell the whole truth on dating websites, embellishing the truth to make themselves more desirable. This example of how OK Cupid users lie about their heights is a good example for conceptually explaining null hypothesis testing, t-tests, and normal distributions.

So, Cheng, article author and data enthusiast, looked through OK Cupid data. In this article, she describes a few different findings, but I'm going to focus on just one of them: She looked at users' reported heights. And she found a funny trend. Both men and women seem to report that they are taller than they actually are. How do we know this? Well, the CDC collects information on human heights so we have a pretty good idea of what average heights are for men and women in the US. And then the author compared the normal curve representing human height to the reported height data from OK Cupid Users. See below...

From, by Fangzhou Cheng 

From, by Fangzhou Cheng

I can think of a number of ways to use this example:

-Null hypothesis testing/effect sizes, in general: Do you control and experimental groups overlap? By how much? Essentially, we are more likely to find significance/large effects the less they overlap. These two figures demonstrate this idea pretty nicely.

-A conceptual example of one-sample t-test. The CDC can provide us with a given number representing average male or female height, which is our known mean/mu. We could then test that number against all of the male or female heights reported by OK Cupid Users. Well, not really test, as we don't have the raw data, but it conveys the idea conceptually.

-This might even make a good example for Social or Evolutionary Psychology.

-Higher level statistics classes could also learn from the code he the author generously shared.

-I remember learning in graduate school that men typically round up when researchers ask them their number of sexual partners, and women typically round down. We can add height to the list of things that people fib about, especially within the context of seeking out a dating partner.

More of Cheng's work can be viewed here.

Wednesday, May 11, 2016

Electronic Conference on the Teaching of Statistics

I hope you are all finishing your grading and enjoying the first bit of your summer vacations (those of you who teach at colleges that run on semesters, at least!). I know that the last thing you want to think about right now is professional development. Especially professional development related to the teaching of statistics. That being said, I wanted to remind everyone that the Electronic Conference on the Teaching of Statistics (eCOTS) is happening next week (May 16-20).

As a mom and generally exhausted person, I really appreciate the chance to attend a conference in my pajamas. Here is the program. It only costs $25, includes workshops and poster sessions, and recordings from the conference. So, even if you aren't mentally ready to tackle something like this right now, you could view the materials at a later date.