As usual, vague terms like "90% accuracy" are thrown around without specifying what exactly they mean. Is that a 10% false positive rate? A 10% false negative rate? 90% accuracy over a representative sample of the general population?
Because the last one is what would seem to be implied, but I have a much simpler program, written in pure JavaScript, that is also 90% accurate at diagnosing Americans with depression: https://gist.github.com/osuushi/5297823
Since, according to the CDC[1], about 10% of Americans are depressed, my script will be accurate 90% of the time.
This is a good point. The parent article doesn't link to the paper but in it they give all of their hypothesis with p values <0.05, so the can reject the null hypothesis and give statistical confidence to theirs:
Comments
As usual, vague terms like "90% accuracy" are thrown around without specifying what exactly they mean. Is that a 10% false positive rate? A 10% false negative rate? 90% accuracy over a representative sample of the general population?
Because the last one is what would seem to be implied, but I have a much simpler program, written in pure JavaScript, that is also 90% accurate at diagnosing Americans with depression: https://gist.github.com/osuushi/5297823
Since, according to the CDC[1], about 10% of Americans are depressed, my script will be accurate 90% of the time.
[1] http://www.cdc.gov/features/dsdepression/
> I have a much simpler program, written in pure JavaScript, that is also 90% accurate at Diagnosing Americans with depression
...well played.
This is a good point. The parent article doesn't link to the paper but in it they give all of their hypothesis with p values <0.05, so the can reject the null hypothesis and give statistical confidence to theirs:
http://schererstefan.net/assets/files/scherer_etal_FG2013.pd...
I think the term you're looking for is "Lies, Damned Lies, and Statistics"