On the other hand reaffirm your priors should mean not putting much weight in Campbell case.
Not sure I understand what is meant by this.
Was trying to say: The data brings value. I don’t trust conclusions drawn from data. I also don’t trust Minger’s belief that the data analysis was obviously flawed because I see many people arguing over that. Rather than investigating further about Minger vs. Cambell on an argument that has experts disagreeing, it’s more worthwhile as a reader to just provisionally assume the data analysis is passable and read more on the topic elsewhere, because the risk of being mislead by flawed data analysis or other methodological issues is ever present, and in a field like this one is better off reading widely and look for broad trends and conceptual replications than one is by reading extremely closely. (And hoping any wrong beliefs brought about by bad data analysis fall away because the other experiments don’t support them.)
That’s what I mean by “low base rate”. If I’m just reading to get a big picture of reality in a field, rather than dive into the difficult rabbit hole of “are the methods ok”, I just operate under the assumption that there’s always an x% risk of any given study being flat-out wrong about everything and keep reading more without worrying about it.
(Which is why “Minger disagrees with those methods” falls into the “well, methods are frequently complicated and controversial and you’ve already factored that in so don’t worry” box. If other experts unanimously chimed in agreement with Minger, or if what she wrote about the flaws seemed obviously true to me, it would be a different matter).
Not sure I understand what is meant by this.
Was trying to say: The data brings value. I don’t trust conclusions drawn from data. I also don’t trust Minger’s belief that the data analysis was obviously flawed because I see many people arguing over that. Rather than investigating further about Minger vs. Cambell on an argument that has experts disagreeing, it’s more worthwhile as a reader to just provisionally assume the data analysis is passable and read more on the topic elsewhere, because the risk of being mislead by flawed data analysis or other methodological issues is ever present, and in a field like this one is better off reading widely and look for broad trends and conceptual replications than one is by reading extremely closely. (And hoping any wrong beliefs brought about by bad data analysis fall away because the other experiments don’t support them.)
That’s what I mean by “low base rate”. If I’m just reading to get a big picture of reality in a field, rather than dive into the difficult rabbit hole of “are the methods ok”, I just operate under the assumption that there’s always an x% risk of any given study being flat-out wrong about everything and keep reading more without worrying about it.
(Which is why “Minger disagrees with those methods” falls into the “well, methods are frequently complicated and controversial and you’ve already factored that in so don’t worry” box. If other experts unanimously chimed in agreement with Minger, or if what she wrote about the flaws seemed obviously true to me, it would be a different matter).