Erik Drysdale discusses and gives some formulas, demonstrating on an example that will be familiar to regular readers of this blog.
Erik Drysdale discusses and gives some formulas, demonstrating on an example that will be familiar to regular readers of this blog.
Very nice piece by Erik Drysdale – thank you for pointing it out.
1. A higher level view of statistical inference was discussed in this blos based on https://ssrn.com/abstract=3591808
2. A broader perspective motivated by the often verbal representation of findings is to apply the S type error. See https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3035070
When he got to “all statistically significant findings are biased”, in bold no less, I lost interest but I’m sure I’ll press on. Nevertheless, I don’t think everyone will be motivated to read a paper that’s clearly factually incorrect in a bolded statement that underlines the premise of the paper, in the first few paragraphs.
I didn’t like that statement either, but it didn’t stop me from going further, and I didn’t regret it. What he means is that generally effect estimates are biased conditional on statistical significance, which is a valid point; later he doesn’t do much market shouting of that kind anymore.
Hi Christian and psyoskeptic,
Thank you for the feedback, I have updated the sentence to make it clearer.