talk about Shakespeare in between complaining about 'the man' and that capitalism not being fair.

Capitalism has been very good to me. I’m a lawyer in his early 40s who makes six figures. Despite your advice, I accomplished this while developing a familiarity with the western canon.

How have you taken advantage of capitalism?

What are the positive effects of marriage for a man? Is there anything he gets from a marriage that he couldn't get while staying single?

Don't ask questions that I answer in my post. Female fertility.

Contraception gave us freedom from being forced to commit just b/c you wanted to fuck a girl and got her pregnant.

No, contraception was one of the ways in which top-down planned depopulation took place. And as they usually do, they couch it in terms like freedom and happiness to make you take their blue pills that go against biological truth and success.

Now you're not after women's fertility, but after their markers of fertility without getting fertility, because birth control pills makes women infertile.

The "perversion of marriage" is another BP belief, namely that women behave like they do b/c marriage is broken. The reality is, women always behaved like this, marriage was for US to believe in, they simply went along with what WE wanted to believe in.

No, I linked a document that you can check the source for where one of the wealthiest and most powerful families in the world had a goal of changing marriage and perception of marriage to cause depopulation. Women were always women, but previously, women shamed other women for being sluts and as such there was a huge social cost for cheating. Now women encourage each other cheat, speaking broadly.

This does not mean women never cheated or marriages were never wrecked and men never raised another man's children. But the factual evidence is literally just against you and I've already provided one piece of evidence for it.

Universal basic income is a way to gain full control over people. It is the second to last step necessary for a mass depopulation event. Because once you control people and they only cost money, what value are you to wealthy and powerful people really? They might let you raise one more generation of loafers, who will be so mal-educated that they'll go into the night forever without a fight, or more likely, helping each other into the night as every previous instance of socialist equality has done and is doing right now in Venezuela.

So you tell me if patriotism is universally right or perhaps "it depends".

Of course it is universally right, as universally right as it is to try and score goals for your own team when playing football and as wrong as it is to try and score goals for the opposing team or try and stand there "neutrally". War is about competition. Although I would go one step further and say that wars aren't and haven't been about benefiting the host nations so much as the puppet masters behind them who can benefit from predicting world events by shaping world events through war.

You didn't address the postmodernist claims at all, but just focused on applying it to marriage.

Anyway - what I never understood is how on Earth can you people (anti-postmodernists to be short) come to the conclusion that since postmodernism is bad, what was before it must be good (or worse - the best). You fail to understand that postmodernism (as was ANY cultural innovation) came to be as a result of previous cultures, not as out of the blue invention.

Anyway - what I never understood is how on Earth can you people (anti-postmodernists to be short) come to the conclusion that since postmodernism is bad, what was before it must be good (or worse - the best). You fail to understand that postmodernism (as was ANY cultural innovation) came to be as a result of previous cultures, not as out of the blue invention. And, as history shows, culture doesn't go backwards.

Postmodernism is bad because if we both fully accepted it, we could not have a conversation about good or bad at all. That's why on a philosophical level, it's the complete antithesis of philosophy itself. And by being anti-philosophical and completely eradicating of examination of good and bad, it's the absolute most evil type of ideology, en par with trying to figure out (non-postmodernisticly) what is bad and making that your ideology.

And, as history shows, culture doesn't go backwards.

Ha ha ha, yes it does, we went from enlightenment to modernism to postmodernism. That's a two pretty big leaps backward.

Do I want to go back to exactly what was before? I'd rather improve on it where possible. But rooting out the corruption introduced into our thinking from the frankfurt school of sociology, among others? Yes, please.

In any case, when someone comes to conclusion that postmodernism is bad, it makes sense that one would prefer an alternate to it. The assumption that every cultural innovation is an objective improvement would imply that no mistakes can ever be made. And every fallen empire in history would disagree with you.

I bet if we had drinks and talked for a while I could figure out what things you do value, and what cultures you do consider corrupt or less valuable, because few are postmodernist to their deepest core and any philosophy that doesn't withstand being embraced that fully is an incomplete one. And then from that center of things you value we'd work outwards.

If there is one thing you value or disvalue, this means value judgements do exists.

If I asked you for evidence why this one thing is better than the other, you could probably provide empirical evidence for it.

And if there is empirical evidence for it and this proves a certain thing to have value, then that can not be held as truthful at the same time as the postmodernist view that everything is relative. It's either / or. You can't have both.

ps: I saw I linked it badly before. In case you missed it, here it is

Ah I know verisatium, and I knew that video a while ago, unfortunately he's wrong, you'll understand why (I will get pretty technical with math, we aware of it)

In science, you make a hypothesis about a fenomena, and then you refute it with an experiment, that's the basics. The problem here is that, no matter how many cases you look, there will be never enough, as you have to be correct 100% of the time, something that isn't possible because you can't analize 100% of the present and future cases. So, what we do, is to say that we are "as close to the truth" as possible, but what does that mean...

Imagine you ask me "what is the height of a human being", I couldn't give you an answer because people have different heights, but what I can do is to give you a range of numbers that covers them, so imagine I say: "Humans are, at least, more than 10cm and less than 5m tall", of course I'm right, but the range I'm choosing is huge, so in order to make comparisons, you have to get the minimal range possible. And not only that, but since you can meassure every human in the present and the future, you'll have to extract those numbers with a sample, that is, a small ammount of people of that population.

So, imagine I meassure the height of 200 people, then I meassure some statistical numbers (mainly central values and how disperse values are from that center), and then I analyze them, but how?

See, if I represent those data from lowest to highest, they tend to make a curve (not always, but for now assume they always do), that is called "Gauss curve", and it is totally measurable. WHat we try to do is to make some math tricks to make that curve be similar to other curves, that have important properties (mainly area surface), the most basic one is z curve, that has a total area of 1, and is "standart" (meaning that it normalizes all values, so you can compare anything, no matter the units, values, etc...), this curve has the important property that, among a certain distance in deviation, comon to any distribution, you can find most of the values (95% and 99% to be exact), this is called confidence interval, and the value that is beyond that is called p.

So imagine I say "average male height is 1'7m, standart deviation is 0,07cm", that means that 95% of males, that are represented with that sample, will have a height between 1'56 and 1'87, and that means that 5% of males with those same characteristics will have other heights, beyond that range, that can't be explained using statistics. You can improve the range, but then data dilutes, because then the range becomes wider, and as I said, it's most useful when it's as thight as possible, so you can compare. Notable and more accurate distributions are pareto distribution (economics and such), anova and t (medicine) and some others not used as often (bimodal distributions, etc...)

Now, that means that a certain part of the population isn't represented there, but wait, because there's more. This is actually not what it's done, because this is used only in descriptive studies.

Now, imagine you have two samples, and you want to compare them, then you do what I mentiones, you have two curves, and then you compare them. You just put them in the same graph, and watch the % of those graphs that are overlapping. So imagine...

Imagine that the shortest man to ever exist is 1'4m, and that the tallest woman to ever exist is 1'2m... if you graph both distributions, you'll see that the upper part of the women's one won't even touch the lower part of the men's one, so 0% is overlapping. This is what's analyzed, and then, because they aren't touching, it's said that both disftributions are different (and as such, both populations are different too, with significance)

Now, imagine that we have two distributions. The criteria to say if they are equal or not is called test of normality, and it calculates a critical value where difference becomes so significant, that it's considered real to 100% of the cases. This significance parameter is called alpha, and in medicine it usually tend to be 0,01, or even lower, but for it to be scientific, it needs to be, maximun, 0,05 (remember the confidence interval and p-value), in mathematics and physics is suer, super low, like... 0,00000000001 or such, that level of significance is immpossible to reach in medicine, as many data would be huge otherwise. Now again, imagine we compare to samples and 1% is shared, that doesn't mean that 1% isn't represented (like in distribution description), it means that 1% isn't know, and that it could be distribution A or B (or in medicine, sane or ill, men or women, effective treatment or not...), but it doesn't say that 1% of those data doesn't correspond, see?

Most sensible and correct normal tests are shapiro-wilk and kolmogorov-smirnov.

Then, we have regression. Now that you know how to compare samples to know the population, you can undestand regression. Imagine i tell you "I'll give you 1 dollar for each even number that you get with this six sided dice", and you roll 1,4,4,3,6; then you probably know how many dollars I owe you. But imagine I tell you "in order to obtain the max value, which numbers would you change, and why", you certainly know the answer. That is because there is a correlation between you rolling an even number and me giving you a dollar, in other words, if you manage to get condition A, then B follows. Now this is where math gets very tricky, as you will see that sometimes very big mistakes can happen. What we do is to plot data, and instead of finding the curve that we used before, now we use other forms, with other properties, the most common is a line (that's called linear regression, but there are others), so if you don't have more points, but you follow that line, then you can preddict those future points. Again, what we really do is to use that point as the average, and then calculate a confidence interval around it: Whatever data I'm missing, it's there.

The problem with regression is that correlation (measured between 1 and 0, representing how good data follows that line) doesn't imply causation, meaning that sometimes there is a very solid correlation, that means absolutely nothing (wearing blue shoes is correlated with car accidents, but you'll understand that this is nonsense), so is up to further analysis to prove the hypothesis. In reality, regression is never used to prove an argument because of this, but it's used to know what to study next, because if there's a correlation, then maybe there's something going on there. another problem (in fact explained by veritasium) is that correlation isn't stable, so maybe your data points in a direction, but in reality, given enough time, it would point to the opposite... this is knows as regression towards the mean, and in medicine they try to solve this by doing every experiment at least 6 times, proving that those 6 times had the exact same results. Given enough time, this problem can be fixed, but there's nothing that can be done about it.

Chi-squared and pearson method are the most used regression analysis, and they're very good in medicine. RMS method is most used in physics, and mathematics don't use models, but isntead, they do their own analysis from scratch

This isn't near enough to cover everything, but at last, I'll tell you that metaanalysis is by far the best evidence work because it analyzes ALL science done in a specific hypothesis, and compares it one by one. It's very rare to find it, because it's very hard to do, but as you can see, there will be no regression towards the mean, population is fully represented, and papers are classified acordingly to the quality of the science done. Next, there's clinical trials, they can be mistaken because of everything said, but they're totally random, so there's no way they have false data (but as I told you, you can still be wrong even with true data); besides, their sample might not be suited for your population (say you want to know how sugar affects fat people... but maybe their sample is full of diabetic people, you can't use that data then, even if it's correct). Then there's other kind of studies, that try to focus on other things, but I'd stay away from them, because they are harder and they are not random