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Focusing on effect sizes! It's much much harder to lie with effect sizes. For instance, in genetics there are entire careers built on an association of certain
by nextos 6y ago
Focusing on effect sizes!
It's much much harder to lie with effect sizes. For instance, in genetics there are entire careers built on an association of certain genetic polymorphism with a particular phenotype. Even if the p-value can be reproduced with huge sample sizes, the effect size of the association is usually tiny.
In other words, if you carry the risk allele the odds of developing a disease are usually only slightly bigger than if you didn't. But this is often not told to funders! Focusing on effect sizes would drive research effort to things that actually make a difference.
You can do effect sizes with something as simple as a confidence interval, or with a fully fledged Bayesian model that tries to estimate the posterior distribution of some parameter.
- kkoncevicius 6y agoModern genome association studies rarely look for single allele effects anymre. The current views shift towards polygenic or sometimes even omnigenic (meaming majority of genes can have indirect effects on the phenotype of disease) associations. No one expects to see big effect sizes because very few people will have differences in any one particular loci. So under these models simple effect sizes are less important than p-values.
- dumb1224 6y agoI think effect size is a very useful tool if you know some odds a priori. E.g if you can compare the effect size with some known features/variables. It can then give you a rough feeling how true it might be. If the features are small as genetic variants then people will have a hard time estimating the supposed effect (unless it's a functional one that alters the encoded protein e.g).