(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Someone asked me about the distinction between bias and noise and I sent him some links. Then I thought this might interest some of you too, so here it is:
Here’s a recent paper on election polling where we try to be explicit about what is bias and what is variance:
And here are some other things I’ve written on the topic:
– The bias-variance tradeoff
– Everyone’s trading bias for variance at some point, it’s just done at different places in the analyses
– There’s No Such Thing As Unbiased Estimation. And It’s a Good Thing, Too.
– Balancing bias and variance in the design of behavioral studies
Finally, here’s the sense in which variance and bias can’t quite be distinguished:
– An error term can be mathematically expressed as “variance” but if it only happens once or twice, it functions as “bias” in your experiment.
– Conversely, bias can vary. An experimental protocol could be positively biased one day and negatively biased another day or in another scenario.
P.S. These two posts are also relevant:
– How do you think about the values in a confidence interval?
(The question was “Are all values within the 95% CI equally likely (probable), or are the values at the “tails” of the 95% CI less likely than those in the middle of the CI closer to the point estimate?”
And my answer was: In general, No and It depends.)
– Why it doesn’t make sense in general to form confidence intervals by inverting hypothesis tests
The post “Bias” and “variance” are two ways of looking at the same thing. (“Bias” is conditional, “variance” is unconditional.) appeared first on Statistical Modeling, Causal Inference, and Social Science.
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