Studies and how to properly interpret them

(ianrobo) #1

whilst @richard @carl this is not directly related to Keto the study (which Robert Lustig posted on FB shows a scientist taking the right approaches to studies, the key paragraph that should be used for ANY study.

Finally, we see a big limitation: This data reveals only correlations, not conclusions. We are left with at least two different interpretations of the sudden spike in “iPhone slow” queries, one conspiratorial and one benign. It is tempting to say, “See, this is why big data is useless.” But that is too trite. Correlations are what motivate us to look further. If all that big data does — and it surely does more — is to point out interesting correlations whose fundamental reasons we unpack in other ways, that already has immense value.

And if those correlations allow conspiracy theorists to become that much more smug, that’s a small price to pay.

(Richard Morris) #2

Love it. Present the correlations, use those to form hypotheses.

The only way to know intent is to do a clinical trial to test the hypothesis. For example to look for code in the new OS release that preferentially slows done older versions.

(ianrobo) #3

and in that example Richard if there was code there meant to force upgrades surely people would have found it by now therefore the conspiracy theory falls due to lack of evidence.

What we get now (in wider world) is plenty of theories but little fact and evidence to back them, so in fact people are lied to.

(You've tried everything else; why not try bacon?) #4

Surely the first step would be to measure the speed of phones before and after a new release to see if the effect is measurable or psychological.