I wish this was more widely understood. People act like "the data tells us" actually holds some weight. And of course there are places where something is so obvious that of course it's reflected in the data. But anything subtle is probably just as subjective as giving an opinion. The more degrees of freedom, the more "the data" can fit whatever somebody wants.

In light of all that, making decisions by gut feelings or intuition is, if nothing else, at least honest, and probably just as good an approach as anything else.

I agree that saying that something is a gut decision is better than pretending that a gut decision was based on data. The former is easier to change.

I don't think intuition tells us much. What we have are perceived interests we seek to rationalise.

If we were all to iteratively acknowledge our interests to a greater degree then we could probably learn a lot more from data but I think there is a strong message in our society that self interest is wrong and so people dedicate great energy to pretending that their self interest does not exist.

> In light of all that, making decisions by gut feelings or intuition is, if nothing else, at least honest, and probably just as good an approach as anything else.

I went down on this thought once, and I became antiscience. Reasoning-wise I only trust what I can understand, simple highschool level reasoning. Anything that I can't understand, and is more sophisticated than a highschool level reasoning I don't trust. I mean, sure, there are facts that need complicated statistical methods. But I can't check them, I don't trust authority, (and probably they are more often wrong than right anyway, because they need results or they starve to death), so I reject them/I am ambivalent.

> there are facts that need complicated statistical methods

Like what? There are ideas that need causal reasoning, or trust that it exists behind things you don't fully understand. Statistics, especially anything higher order, end up being basically just rhetoric, outside maybe of some very narrow claims.

If someone tells you you need a bunch or statistics to understand something, they're almost certainly trying to persuade you without having a strong enough logical footing to just explain themselves.

And science isn't a religion or a position, you can't really be for or against it. That should be a starting point.

For example, understanding why certain estimators give you the average treatment effect on treated individuals rather than the average treatment effect on everyone is easier if you understand the mathematics of it. But you need to know basic probability and mathematical statistics.

Causal mediation analysis is similar if you want to understand what assumptions you really need to make to talk about the mechanisms through which a treatment effect acts on the outcome.

I rest my case. If someone told me that the only way I was going to notice something is with the stuff you are saying, I'd be pretty reluctant to trust them.

If you're talking about optimizing around some agreed upon thing, sure I can understand the idea of using some more complex statistical analysis (though I might question the incremental value).

I am not saying that a convincing answer has to be convoluted. But a simple answer is not always the right one.

To understand when a simple answer is unlikely to be correct, you do benefit from understanding the mathematics. There’s a reason that people with doctorates in statistics spend 8 to 10 years in school to learn how to contribute to it.

I made the c-suite SaaS metrics generator especially for those types of managers!

https://github.com/patricktrainer/csuite-saas-metric-generat...

change upper management to politicians and you have an accurate description of modern science

Are you talking about vaccines?

Words mean things. Neuroscientists study the mind.

Did the comment I replied to mention neuroscience? Please put your vote back

Indentation of comments also means something. GP was not responding to the neuroscience comment.

I was a neuroscience researcher for 15 years, and the opinions or recommendations of politicians never had anything to do with what I did.

Modern science isn’t like that at all.

It’s public perception of modern science that is that way (in large part because of politicians).

Still bad, but a different kind of bad.

This is why I'm reluctant to go into data science, information security, etc. I feel like I will just be disappointed and disenchanted.

Unfortunately the case -- you just have to be honest and say "no it can't do that" when it can't. The "impedance mismatch" between managers/stakeholders and contributors is an ever renewing source of BS for many of us.