For a bit of levity, here is Peter Hitchens being hilariously wrong about mathematics for the second time this year
It's called a relative risk, and while imperfect it is the most common way to sum up such a difference
pic.twitter.com/JvTJGYnidn
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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For a bit of levity, here is Peter Hitchens being hilariously wrong about mathematics for the second time this year
It's called a relative risk, and while imperfect it is the most common way to sum up such a difference
pic.twitter.com/JvTJGYnidn
It's a margin calculation thats not mentioned in the original research. The difference is actually 9 cases in population > 6000 - a much better indication than plucking a relative 14% figure from the ether.
I'm not sure what you mean. The original study reported both absolute and relative risk differences and confidence intervals as is appropriate
The % of % type calculation which this is, loses all information about original sample size, so is of limited value. Please link me to the study reference to 14%.
Technically it's an odds ratio rather than relative risk, but the principal is the same. What you are claiming is statistically nonsensicalpic.twitter.com/84Xp5fJWdE
I've see that ( where is the 0.3/2.1 % calculation ? ), and I'm claiming nothing other than the limited usefulness of the double % calculation in this situation, so in what sense is that nonsense ?
Because the numeric value of absolute risk is just as meaningless without the numeric context as the absolute risk. And honestly if you're going to argue statistics I'd suggest doing some basic reading on things like odds ratios
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