my first guess is that it was a militarist gunman who was unhappy with not-enough-militarism from Abe, and, there must be a social group associated here, everything is social, including being a militarist. real information welcome
I think the parent is answering OP's question - which is about the _gunman_'s justification. The parent explaining behavior is not the same thing as the parent defending or justifying it.
Japan's stance of war crimes has been a point of tension between Japan and its East Asian neighbors.
These are matters of nationalism and affect forward looking geopolitical outcomes.
Following WWII, a tribunal was held in Tokyo along the lines of trying German war criminals. You can imagine how celebration of what others perceive to be German war criminals could lead to conflict.
We were fairly aware of our usage and strain potency back in University in 2007. But this just might be what happens when you get a bunch of stoner biology, engineering, and tech students all living together.
I think people are criticizing this checkbox in a vacuum. This is a mobile app that I personally use to track a small number of habits a day, it's not like I'm filling out a form on a PC. Within the context, it's definitely an appropriate design
I think it would be the opposite. Someone who is taller than you would on average look more like their head is tilted up from your perspective, and vice versa for someone shorter than you.
I think it comes from the statistical expectation. Like, if you were able to donate your kidney any amount of times, you would on average expect to lose your life on the 3,000th donation. So the question is, do you want to be the kind of person who would do that at the cost of your own life?
Again though, they are arguing that there is no correlation between randomness and age. This was just a demonstration that when they use randomness to predict age, the results are wrong 50% of the time-- which is precisely in accordance with their hypothesis
Yeah but their guess shouldn't be wrong 50% of the time as again that means that they can’t have picked the 95th percentile result! Because it’s 50:50 I’ll assume that they are assigning people scoring higher than average the “under 60” category - which is obviously incorrect. Otherwise how do they pick the cut off?
To explain with another example - let's say that I have a dataset of 100 people's scores at golf (no handicaps) and I know that 5% of them are pro-players and others are 'advanced amateurs'. Because of this I might take the top 5 scores and guess that they are pro's and assign the others the guess of 'advanced amateur'.
Now let's say that there was actually no correlation between people's scores at golf and their 'pro' status - what accuracy would I expect in the above experiment? The answer is actually closer to 90% 'accurate guesses' than 50%! (Although obviously - that's 90% accurate based on random chance).
Now if someone told me they got 50% of the guesses wrong at this task, that implies that they guessed that the top 50% of those golfers were pro rather than picking the top 5% of scores, and I would question the methodology.
This % is similar to the dataset in the webpage - I downloaded it, filtered out exclusions and c4% of the valid responses are 60 or over.
If I inherently pick a small population (i.e. over 60's are c4% in this dataset) and I am guessing wrong 50% of the time, it means that my cut-off is incorrectly calibrated. Their score cut-off should, at worst, be picking the wrong 4% and missing another 4%.
Am I going crazy? It seems logical to me, but to be open maths isn't my strong point. I just know that if I designed the guessing rule, I would be getting more than 50% (my algorithm would be 'if the users average score across the three tests is less than -1.5, assign 'over 60' and that would get c95% accurate guesses, albeit it would still not prove anything and I agree with the authors overall premise!).
In your golf example, making that guess requires an additional knowledge of what "pro" means and it's frequency among golfers. The data doesn't know that just like the randomness data doesn't know that most humans are younger than 65 years old. If you really want to figure out how predictive the data is, you shouldn't include considerations like that in your model. I get what you're saying but ultimately I don't think their goal was to make the most accurate prediction, they wanted to make one that illustrated their point by basing their guess off the data alone.
The calculation involves knowing the age of the sample population though (if you don’t know the ages of your sample, how do you work out what the cut off is at 60 years?).
If I don’t know how many golfers are pro, I simply cannot estimate if it is 100 golfers that are pro or 0 (unless it’s a real gap in scores). Making an assumption that 50 are pro is no more valid than 0 or 100.
If you take the average score of 100 people and say that you estimate anyone scoring below the average is above 60, you are going to be wrong regardless of if your hypothesis is valid or not.
Putting that up and saying “see, it’s wrong 50% of the time!” doesn’t make sense when your calculation is incorrect.
In order to calculate the cut-off correctly they either need to take the 95th percentile result, or pick a sample where 50% of people are over-60 and 50% are under 60 and take an average of that.
Using a dataset where 95% of people are under 60 and then picking the mean clearly isn’t going to work.
No, I think he is saying an oligarch that has a lot of focus on the rest of the oligarchically controlled media taking control of this piece of media already controlled by the haut bourgeois oligarchy will draw more public attention to the oligarchic control of the media without actually changing the fact of that control one bit.
Which is still, IMO, foolish, given, among other things, the degree to which large swathes of the public have parasocial relationships with the particular celebrity oligarch in question, but it's not saying that making the problem worse will draw attention.
