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Aug

2011

Any set of figures needs adjusting before it can be usefully reported | Ben Goldacre | Comment is free | The Guardian

I will now demonstrate, with a nerdy table illustration, how you correct for things such as social and demographic factors. You'll have to pay attention, because this is a tricky concept; but at the end, when the mystery is gone, you will see why reporting the unadjusted figures as the finding, especially in a headline, is silly and wrong.

www.guardian.co.uk/...bad-science-adjusting-figures

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Statistics Headline Journalism Science Communication

reporting the unadjusted figures as the finding, especially in a headline, is silly and wrong.
2 more annotation(s)...
- Correcting for an extra factor is best understood by doing something called "stratification".
- in the real world of research, you're often correcting for lots of things at once. In the case of this paper, they corrected for parents' socioeconomic position and qualifications, sex of child, age, language spoken at home, and a huge list of other factors.
  When you're correcting for so many things, you can't use old-fashioned stratification, like I did in this simple example, because you'd be dividing your data up among so many smaller tables that some would have no people in them at all. That's why you calculate your adjusted figures using cleverer methods, such as logistic regression, and likelihood theory. But it all comes down to the same thing. In our example above, alcohol wasn't really associated with lung cancer. And in this BMJ paper, unplanned pregnancy wasn't really associated with slower development. Pretending otherwise is just silly.