4/n Digging into the site, you're immediately hit with this error. That's not how p-values work at all, any stats textbook will show you why this statement is entirely untruepic.twitter.com/Hzb4K1NYaH
Epidemiologist. Writer (Guardian, Observer etc). "Well known research trouble-maker". PhDing at @UoW Host of @senscipod Email gidmk.healthnerd@gmail.com he/him
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4/n Digging into the site, you're immediately hit with this error. That's not how p-values work at all, any stats textbook will show you why this statement is entirely untruepic.twitter.com/Hzb4K1NYaH
Hi, Health Nerd. I'm a statistican and textbook author. I computed the p-value and found the exact answer here. There is nothing about the statement I can see that is incorrect. A p-value is the result of the computation testing to see if a result could occur by random chance.
Perhaps you're reading in a meaning the author meant to express but didn't. e.g. perhaps they meant to say "a hypothetical ineffective treatment would generate" rather than "an ineffective treatment generated"
It's also worth noting that in the context of this meta-analytic model the p-value is entirely the result of the cherry-picking of "positive" values, so the chance of having a low p-value is 100% regardless of whether ivm works or not
No, there was no cherry-picking. There was an extremely forgiving set of inclusion-exclusion criteria that let in some positive and negative results, but left out almost nothing.
Of course there is cherry-picking throughout, it is rather boringly obvious. The anonymous authors of the website simply pick the most convenient values for their analysis so that they can have a better looking model regardless of severity etc
I identified one example of this in the thread, but it's pretty much ubiquitous throughout the analysis
Can you name the author in the example of cherry-picking? I don't see it. Which study was misplaced in the inclusion-exclusion criteria?
It's not the inclusion criteria, which are basically "chuck all the awful studies into one website". It's just that the authors extract only "positive" results regardless of whether studies actually showed a benefit
You keep conflating "didn't show a benefit" with "wasn't statistically significant", but the latter doesn't make a difference in a binary p-value computation.
That's not what I'm conflating actually. I'm simply saying that the authors pick the most "positive" result, regardless of statistical significance, ignoring results where ivm did worse even if those results are by their own methodology the ones that should have been chosen
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