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I admit I'm confused about what the author is arguing. They seem to be arguing that we should just adopt a strict causal predictive approach to data? I'm not

I admit I'm confused about what the author is arguing. They seem to be arguing that we should just adopt a strict causal predictive approach to data? I'm not sure what that really means though. They seem to be just assuming that existing observations are the population? They seem to be missing that every single estimation is a form of prediction. For example, if you estimate a regression slope parameter, you're making a prediction about the degree and nature of association of two variables in future samples. The parameter is a random variable over possible observation samples. That parameter estimate, together with the associated model (e.g., bivariate normal observations) can be translated into a specific bivariate probability distribution prediction. Lots of interesting issues are discussed but I'm not really sure they understand what they're advocating for. Statistics is always about prediction at some level, or can be formulated that way. Bayesians believe that data-based updates to models (which are used to predict) should be influenced by information available prior to the data; frequentists do not.

throwaway81523 6y ago
> I admit I'm confused about what the author is arguing. I suspect the author is also confused, based on the length and repetitiveness of the article. Let's say you flip a coin, have it land on the back of one of your hands, and quickly cover it with your other hand so you can't see how it landed. Despite that, it is no longer spinning: its state is now completely determined. So what is the probability P that the coin came up heads? In one way to look at it, P=50%, assuming it was a fair coin that generated a uniformly distributed binary variable. In another way, P is either 100% (the coin in fact came up heads) or else it is 0% (coin came up tails), and "50%" is meaningless: it's not Schroedinger's coin. So in this view, people who say 50% are committing the error of (insert long essay here full of fancy words like "epistomology"). Philosophers write books about this issue, plato.stanford.edu has some good articles about probability interpretation that also discuss the topic, and the linked article is more of the same. For the rest of us, there are some basic confusions that beginners and non-mathematically-informed people make, but if you take a few classes or work through a basic textbook, you can understand how significance testing works, what confidence intervals actually measure, etc, and then you can think pretty clearly. The Bayesian vs frequentist thing is a yawner: if you put one of each in a room, give them the same data and ask them to crunch the numbers, they might disagree on some philosophical abstractions but they'll get the same answers. I find Andrew Gelman's blog far more interesting than the linked one, at least based on the linked article. Either way, it's definitely worth studying some basic probability theory (understanding the math rather than trying to interpret the topic by navel-gazing) if you're a programmer or do anything else in science or technology.

throwaway81523 6y ago

> I admit I'm confused about what the author is arguing. I suspect the author is also confused, based on the length and repetitiveness of the article. Let's say you flip a coin, have it land on the back of one of your hands, and quickly cover it with your other hand so you can't see how it landed. Despite that, it is no longer spinning: its state is now completely determined. So what is the probability P that the coin came up heads? In one way to look at it, P=50%, assuming it was a fair coin that generated a uniformly distributed binary variable. In another way, P is either 100% (the coin in fact came up heads) or else it is 0% (coin came up tails), and "50%" is meaningless: it's not Schroedinger's coin. So in this view, people who say 50% are committing the error of (insert long essay here full of fancy words like "epistomology"). Philosophers write books about this issue, plato.stanford.edu has some good articles about probability interpretation that also discuss the topic, and the linked article is more of the same. For the rest of us, there are some basic confusions that beginners and non-mathematically-informed people make, but if you take a few classes or work through a basic textbook, you can understand how significance testing works, what confidence intervals actually measure, etc, and then you can think pretty clearly. The Bayesian vs frequentist thing is a yawner: if you put one of each in a room, give them the same data and ask them to crunch the numbers, they might disagree on some philosophical abstractions but they'll get the same answers. I find Andrew Gelman's blog far more interesting than the linked one, at least based on the linked article. Either way, it's definitely worth studying some basic probability theory (understanding the math rather than trying to interpret the topic by navel-gazing) if you're a programmer or do anything else in science or technology.