lottin

4 maanden geledendiscuss

Statistical inference is based on random sampling. The data has to be random, otherwise it doesn't work.

I wrote another comment here clarifying my point, if you're interested: https://news.ycombinator.com/item?id=47566033

lottin

4 maanden geledendiscuss

Imagine we want to know the ratio of men to women in a particular population. We could count all men and women one by one, but it would take too long, so instead we take a random sample and count the men and women in the sample, and from that we infer the quantity that we want to know. This is statistical inference.

In Bayesian inference, the population ratio is seen as a quantity that can take different values each with a associated probability (i.e. a random variable), and the result of Bayesian inference is an estimate of the probability distribution of the population parameter, in this case the population ratio. Now, in reality the population ratio is a concrete number, say 9-to-10, meaning that there are 9 men for every 10 women in the population. But Bayesians don't care. They'll tell you that the population ratio is a random variable which can take many values, and that the probability that it is equal to 9-to-10 is whatever number between 0 and 100%.

This is nonsense because the population ratio is NOT a random variable. People don't come in and out of existence randomly, right? In a way, they're saying there are infinitely many possible universes, each with a different population ratio, and then they come up with an estimate of the probability that the universe in which the ratio is 9-to-10 has whatever probability of occurring. This is absolutely BIZARRE. (I hope you agree). And it's wrong because it's impossible to know how likely one universe is compared to all other possible universes, since we live in our universe and this is all we can hope to observe.

lottin

4 maanden geledendiscuss

> Bayesian inference assumes the observed data are fixed and aims to quantify the evidentiary support for all possible levels of treatment effectiveness based on the data at hand.

The problem with this approach is that we can only observe ONE level of treatment effectiveness, i.e., the level of treatment effectiveness that the treatment actually possesses. All other possible levels of effectiveness are entirely hypothetical. There's no data about all these other possible levels of effectiveness because they don't occur in reality. So the data cannot possibly tell you anything about how likely is the observed outcome, because the observed outcome is the only outcome that you observe. I

This criticism was made over 100 years ago, and Bayesians still don't have an answer. They just keep going as if nothing happened, but the reality is their methodology is utterly and fatally flawed.

lottin

4 maanden geledendiscuss

> In Bayesian statistics, on the other hand, the parameter is not a point but a distribution.

To be more precise, in Bayesian statistics a parameter is random variable. But what does that mean? A parameter is a characteristic of a population (as opposed to a characteristic of a sample, which is called a statistic). A quantity, such as the average cars per household right now. That's a parameter. To think of a parameter as a random variable is like regarding reality as just one realisation of an infinite number of alternate realities that could have been. The problem is we only observe our reality. All the data samples that we can ever study come from this reality. As a result, it's impossible to infer anything about the probability distribution of the parameter. The whole Bayesian approach to statistical inference is nonsensical.

lottin

9 maanden geledendiscuss

Retained earnings are not taxed per se. A company pays taxes on profits. Whether the profits are distributed to shareholders or retained makes no difference whatsoever as far as taxes are concerned.

lottin

10 maanden geledendiscuss

How does exactly "breaking windows" improve the lives of people?

lottin

10 maanden geledendiscuss

I can't imagine a situation in which I'd want to explain what I want to do on the command line to an LLM, instead of typing the commands myself.

lottin

10 maanden geledendiscuss

I wish the scroll bar was a little less invisible.

lottin

10 maanden geledendiscuss

As expected. This is why we don't use nominal dollars for measuring changes in prices over long time periods. It's meaningless.

lottin

10 maanden geledendiscuss

I think it's just a meaningless sentence.

lottin

10 maanden geledendiscuss

If I deposit dollars in a savings account I will get paid interest, but that is different from the dollar itself being an interest-bearing asset. I think the same thing applies to stablecoins. Does USDC pay interest to the holder or do I have to make a USDC deposit at Coinbase in order to get paid interest? Also, banks already offer a ton of products that generate yield. I don't see why a product that seems relatively similar to many products that banks already offer would destroy their business... unless such a product is much better than what banks offer, but that doesn't seem to be the case.

lottin

10 maanden geledendiscuss

Considering that stablecoins don't pay interest to the holder, I don't know why anyone would be incentivised to move their funds into stablecoins.

lottin

10 maanden geledendiscuss

Why would a stablecoin granting yield keep the banking system from working?

lottin

11 maanden geledendiscuss

The whole point of working out is to stress the organism in order to induce a physiological adaptation. Inflammation is NOT the point, but rather an unfortunate side effect.

lottin

11 maanden geledendiscuss

By definition the average answer will be average, that's kind of a tautology. The point is that figuring things out is an essential intellectual skill. Figuring things out will make you smarter. Having a machine figure things out for you will make you dumber.

By the way, doing a better job than the average human is NOT a sign of intelligence. Through history we have invented plenty of machines that are better at certain tasks than us. None of them are intelligent.

lottin

11 maanden geledendiscuss

When I have a question, I don't usually "ask" that question and expect an answer. I figure out the answer. I certainly don't ask the question to a random human.

lottin

11 maanden geledendiscuss

I think the R standard library is quite excellent. It pretty much follows the Unix philosophy of "doing one thing right". The only exception being `reshape` which tries to do too many things, but it can usually be avoided. It isn't inconsistent. I think the problem is the lack of tutorials that explain how to use all the data manipulation tools effectively, because there are quite a lot of functions and it isn't easy to figure out how to use them together to accomplish practical things. Tidyverse may be consistent with itself, but it's inconsistent with everything else. Either you only use tidyverse, or your program looks like an inconsistent mess.

lottin

11 maanden geledendiscuss

I didn't rewrite the whole thing. But here's the first part. It uses the `histogram` function from the lattice package.

    population_data <- data.frame(
        uniform = runif(10000, min = -20, max = 20),
        normal = rnorm(10000, mean = 0, sd = 4),
        binomial = rbinom(10000, size = 1, prob = .5),
        beta = rbeta(10000, shape1 = .9, shape2 = .5),
        exponential = rexp(10000, .4),
        chisquare = rchisq(10000, df = 2)
    )
    
    histogram(~ values|ind, stack(population_data),
              layout = c(6, 1),
              scales = list(x = list(relation="free")),
              breaks = NULL)
    
    take_random_sample_mean <- function(data, sample_size) {
        x <- sample(data, sample_size)
        c(mean = mean(x), sd = sqrt(var(x)))
    }
    
    sample_statistics <- replicate(20000, sapply(population_data, take_random_sample_mean, 60))
    
    sample_mean <- as.data.frame(t(sample_statistics["mean", , ]))
    sample_sd <- as.data.frame(t(sample_statistics["sd", , ]))
    
    histogram(sample_mean[["uniform"]])
    histogram(sample_mean[["binomial"]])
    
    histogram(~values|ind, stack(sample_mean), layout = c(6, 1),
              scales = list(x = list(relation="free")),
              breaks = NULL)

lottin

11 maanden geledendiscuss

A "pipe" is simply a composition of functions. Tidyverse adds a different syntax for doing function composition, using the pipe operator, which I don't particularly like. My general objection to Tidyverse is that it tries to reinvent everything but the end result is a language that is less practical and less transparent than standard R.

lottin

11 maanden geledendiscuss

I disagree. In this example tidyverse looks convoluted compared to just using an array and apply. ggplot2 is okay but we already had lattice. Lattice does everything ggplot2 does and produces much better-looking plots IMO.

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