• facial bias
  • experiments show that children and adults alike who didn't know any of the faces shown, chose actual election leaders and runner ups of elections to be their leaders
  • China exploits the "white-guy-in- a-tie" problem to win deals.
    • Companies hire a white person with zero experience to wear a nice suit and tie and pose as a businessman who has just flown in from Silicon Valley.

• key observation
  • the dynamics and relationship between
    • self-selection bias and
    • survivorship bias
  • gives us insight of how power operates
  • The wrong kinds of people who are power-hungry, seek power more in the first place.
  • Then they're better at obtaining it.
  • They show up in our ordinary lives because they've survived,
    • they've made it.
  • So when we think about who is powerful,
    • we have to think about
      • the people who didn't seek power in the first place and
      • the people who didn't obtain power in the first place.
      • the people who didn't survive in power for very long, and therefore they dropped out.
    • The presidents and prime ministers,
    • the generals,
    • the cult leaders,
    • the business leaders,
  • those people are basically people who have survived and who self-selected.

• key observation
  • People who are power-hungry, people who are psychopaths
    • tend to self-select into positions of power more than the rest of us.
  • And as a result, we have this skew, this bias in positions of power
    • where certain types of people, often the wrong kinds of people,
    • are more likely to put themselves forward to rule over the rest of us

Richard McElreath 🍜 on Twitter: “Everything is selection effects, always has been. From page 162 of my book: Https://t.co/tQaeF2LXkW” / Twitter. (n.d.). Retrieved June 28, 2021, from https://twitter.com/rlmcelreath/status/1396040993175126018

I think this paper and these data could be extremely useful for psychologists, but I also think this paper needs at least one more analysis: estimating effect sizes by research area, controlling for publication bias.

It's very hard to interpret these estimates given good evidence and arguments that researchers and journals select for p < .05. I think it's safe to assume that all these estimates reported in this preprint are bigger than the true averages (Simonsohn, Nelson, & Simmons, 2014).

One approach to estimating "selection bias adjusted" effects would be to estimate the effect size for each research area using the code provided in the p-curve effect size paper supplements (http://www.p-curve.com/Supplement/). You could estimate confidence intervals or percentiles using bootstrapping procedures or write code to estimate the lower and upper bounds using the same methods to estimate the average effect.

This approach assumes the p-values all test the hypothesis of interest and don't suffer from unique selection biases (see Selecting p Values in Simonsohn, Nelson, & Simmons, 2014, p. 540).

Hope this helps make the paper better!