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I feel like a lot of the comments here are written after only taking the test and many are not reading the rest of the article. The authors of the website are

I feel like a lot of the comments here are written after only taking the test and many are not reading the rest of the article. The authors of the website are stating that they believe the study is wrong. The below/above 60 answer is showing you it’s incorrect half of the time along with data backing up the claim.

gryson 4y ago
Yes, hilarious comments in this thread. Please at least skim the article.
- Brybry 4y ago
  The end of the article was hilarious. > we decided to reduce our experiment to three tasks because of attention spans (not yours, it is exceptional if you are reading this).
Closi 4y ago
But their data doesn't make sense to be personally... Only 5% of their dataset is above the age of 60, making their claim that they are getting 50% of their guesses wrong seem like they are calculating it wrong. Surely their cut-off should be at the 95th percentile of the data? They shouldn't be guessing 'under 60' the same proportion of times as 'over 60', because their population is mostly under 60.
- prolyx 4y ago
  Yeah, they would be far better off just guessing under 60 every time...
- sohdas 4y ago
  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
  - Closi 4y ago
    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!).
    - sohdas 4y ago
      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.
Hbruz0 4y ago
I lol'd at the "Trend line": https://imgur.com/a/ohYbcLL https://imgur.com/a/ohYbcLL
- d33k4y 4y ago
  https://xkcd.com/2048/ https://xkcd.com/2048/
msrenee 4y ago
I'd have read it if it weren't white text on a pink background. I'm not going through the trouble of pulling it up in a browser and undoing what they presumably did on purpose. Then to complain that people don't read the whole thing?

gryson 4y ago

Yes, hilarious comments in this thread. Please at least skim the article.

Brybry 4y ago
The end of the article was hilarious. > we decided to reduce our experiment to three tasks because of attention spans (not yours, it is exceptional if you are reading this).

Closi 4y ago

But their data doesn't make sense to be personally... Only 5% of their dataset is above the age of 60, making their claim that they are getting 50% of their guesses wrong seem like they are calculating it wrong. Surely their cut-off should be at the 95th percentile of the data? They shouldn't be guessing 'under 60' the same proportion of times as 'over 60', because their population is mostly under 60.

prolyx 4y ago
Yeah, they would be far better off just guessing under 60 every time...
sohdas 4y ago
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
- Closi 4y ago
  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!).
  - sohdas 4y ago
    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.

Hbruz0 4y ago

I lol'd at the "Trend line": https://imgur.com/a/ohYbcLL https://imgur.com/a/ohYbcLL

d33k4y 4y ago
https://xkcd.com/2048/ https://xkcd.com/2048/

msrenee 4y ago

I'd have read it if it weren't white text on a pink background. I'm not going through the trouble of pulling it up in a browser and undoing what they presumably did on purpose. Then to complain that people don't read the whole thing?