Statistical literacy is important. I love examples of folks using statistical concepts without being snobby statistical gatekeepers. For instance, see jargon-free statistical literacy that helps people make important decisions about their children's health or informs the immigration debate . As stats teachers, I think we are remiss if we don't talk about political polling in our classes. At least a little bit. Here is a brief, nonpartisan interview about political polling you can use in class to see whether students can identify the concepts they've learned. Here is a short NPR i nterview o n how to think properly about political polling. If you use the interview in class, you could ask your students to identify the statistics and research methods mentioned. I clocked margin of error, sampling error, and research methodology evaluation. If using an audio interview isn't your thing, check out t his summary piece from NPR, based on the...
This NPR story is about reproducibility in science that ISN'T psychology, the limitations of expert intuition, and the story is a summary of a recent research article from PLOS Biology (so open science that isn't psychology, too!).
Thrust of the story: Cancer researchers may be having a similar problem to psychologists in terms of replication. I've blogged this issue before. In particular, concerns with replication in cancer research, possibly due to the variability with which lab rats are housed and fed.
So, this story is about a study in which 200 cancer researchers, post-docs, and graduate students took a look at six pre-registered cancer study replications and guessed which studies would successfully replicate. And the participants systematically overestimated the likelihood of replication. However, researchers with high h-indices, were more accurate that the general sample. I wonder if the high h-indicies uncover super-experts or super-researchers who have been around the block and are a bit more cynical about the ability of any research finding to replicate.
How to use in a stats class: False positives: The original research didn't replicate (this time, maybe) AND that the experts judging replicability were overly optimistic. Also, one might wonder if there are potential cancer treatments that we don't know about because of false negatives.
How to use in a research class: The lack of reproduction may signal evidence of the publication bias. Replication is necessary for good science. Experts aren't perfect.
Thrust of the story: Cancer researchers may be having a similar problem to psychologists in terms of replication. I've blogged this issue before. In particular, concerns with replication in cancer research, possibly due to the variability with which lab rats are housed and fed.
So, this story is about a study in which 200 cancer researchers, post-docs, and graduate students took a look at six pre-registered cancer study replications and guessed which studies would successfully replicate. And the participants systematically overestimated the likelihood of replication. However, researchers with high h-indices, were more accurate that the general sample. I wonder if the high h-indicies uncover super-experts or super-researchers who have been around the block and are a bit more cynical about the ability of any research finding to replicate.
How to use in a stats class: False positives: The original research didn't replicate (this time, maybe) AND that the experts judging replicability were overly optimistic. Also, one might wonder if there are potential cancer treatments that we don't know about because of false negatives.
How to use in a research class: The lack of reproduction may signal evidence of the publication bias. Replication is necessary for good science. Experts aren't perfect.