sin7

There's a free community version.

I misread your comment. Sorry about that. I misread it because I think of a generation as twenty years and a lifetime as what you described. Sorry again.

De facto is not the same as official.

Lies, damn lies and then this. 85% counts Hispanic people as white. The 35% does not. Ain't no way this is an innocent mistake. Hispanic people have always been a large portion of California.

If a 'fair' coin came up 70% you lose, you'd be ready to fight the person trying to scam you. At the very least ready to call the cops on them.

Yeah. So?

Make no mistake about it, there is no argument. We are not negotiating. I'm not recognizing anything that was said. Like hearing my nephew cracking up over saying poop, it was cute the first time. It's time for new material yungin'.

Even worse, a good portion of the population believes that a good portion now believes that ... whatever you just said. And this good portion is annoying. Bring up nonsense like this in conversations where it does not fit, in the middle of dinner, in the middle of pumping gas. Like damn, I'm watching out for one of those kids with a gun just waiting to exhibit his mental health and you want me to worry about the guy in a dress?

Sir, you have made my day. This is the funniest thing related to Joe Rogan.

Thank you.

Luka is currently playing in his 5th year. He has 4 years behind him. Not the best comparison, but seems fair.

Can poor people take out a loan and create new money?

No need. Just attribute an unmeasurable quality such as creativity to your racial group.

Whites also have lower average scores on them. Lower than Asians and Jews.

At the elite level this would make a huge difference. At the state level not at all.

I already have a machine that makes white castle burgers at home. It's called a microwave. The results are not the best.

    runs <- 100000
    x <- vector(mode = "numeric", length = runs)
    for (i in 1:runs){
      while (sum(sample(1:8, size = 3, replace = TRUE)) != 24){
        x[i] <- x[i] + 1
      }
    }

    summary(x)
     Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
      0.0   146.0   353.0   511.8   708.0  5112.0 

    quantile(x, c(0.5, 0.8, 0.9))
     50%  80%  90% 
     353  824 1187

Strangely enough the mean agrees. The other ntiles are off a bit, but that's randomness for you.

    runs <- 10000
    x <- vector(mode = "numeric", length = runs)
    for (i in 1:runs){
      while (sum(sample(1:6, size = 3, replace = TRUE)) != 18){
        x[i] <- x[i] + 1
      }
    }

    summary(x)
    quantile(x, c(0.5, 0.8, 0.9)) 

    > summary(x)
       Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
        0.0    62.0   149.0   216.2   300.0  1902.0
    > quantile(x, c(0.5, 0.8, 0.9))
    50% 80% 90%
    149 350 495

A simple simulation. Run 10K times. Count the number of times it takes for three dice to add up 18.

The numbers very much agree with you. The median is 149. The 90th is 495 in the simulation, which is close enough to 496. There is very much a long tail in the data. So, the median and the average will not be the same. Is it a coincidence that mean is a 216?