3 ms·

> (...) and they keep winning That's the textbook definition of survivorship bias. It's like a lottery winner boasting about his winning strategy that everyone

> (...) and they keep winning That's the textbook definition of survivorship bias. It's like a lottery winner boasting about his winning strategy that everyone else is just not able to learn.

bumby 2y ago
I read the OP differently. I think there can be many angles to a person's identity makeup and they don't always cohere perfectly. A person can have their identity in "entrepreneurial libertarian" while also having it as "someone who comes from a family valuing military service". Humans aren't always perfectly rational when it comes to their different values/tribal identities. There are a lot of people who join the military while simultaneously "hate authority" for example.
- chipdart 2y ago
  My answer was not touching on the topic of identity. I'm referring to the relationship between cause ad effect.
  - bumby 2y ago
    That is still downstream of the question of “why would someone make such a [seemingly contradictory] decision.” The person you replied to is misunderstanding the OP, which they later clarified. My point is understanding outcome causality doesn’t necessarily have to even enter the decision.
bluGill 2y ago
At what point do we conclude the lottery isn't as random as they claim if one person keeps winning. Statistically someone will win, there are good odds someone will win twice, but the odds of anyone winning 3 times is almost zero.
- chipdart 2y ago
  > At what point do we conclude the lottery isn't as random as they claim if one person keeps winning. The whole thing about survivorship bias is that you make a critical failure in analysis when confusing partial observations of post-facto results with causality.
  - bluGill 2y ago
    The point of statistics (one of many) is to figure out how many observations we need. If someone wins the lottery 10 times with their system I will assume that they have a good system (if they have a lot of losses as well it means the system isn't perfect, but it still works), but if you only win once and never enter again I assume it is survivorship basis. Of course by winning the lottery I mean win a large jackpot - most have smaller prizes that you have high odds of winning many times if you play often enough.
    - manwe150 2y ago
      Only if you only play 3 times though (in your previous example). Statistics also are about figuring out what sort of outliers must exist for a process to be fair (true random). For something like a mega lottery with terrible odds, then winning twice is already very unlikely. But for something easy like a coin flip, every N trials should have a run of about sqrt N heads or wins in a row if it is unbiased. For something unlikely like lotto, it is closer to looking at the birthday paradox: the probability of one person winning twice is low; but the probability that there exists a person who won twice is high, at random.
    - chipdart 2y ago
      > The point of statistics (one of many) is to figure out how many observations we need. No, you're missing the whole point. Think about the problem about survivorship bias. Imagine you are at a M&Ms factory. You decide you want to assess what's the color distribution of M&Ms by sampling the colors that come out of the production line. You somehow make the mistake of sampling the production line for the peanut core M&Ms right out of the pipe that produces yellow M&Ms. You sample away and after hours you present your findings: 99.9% of yellow M&Ms have a peanut core. Based on your findings, you proceed to boldly claim that having a yellow core is a critical factor in producing yellow M&Ms. You even go as far as to rationalize it, and claim that yellow represents peanuts, and if anyone wants to create yellw-colored candy they need to start by adding peanut to the mix. I then alert you to the fact that you made a critical failure in analysis when confusing partial observations of post-facto results with causality. Your answer: > The point of statistics (one of many) is to figure out how many observations we need. If someone wins the lottery 10 times with their system I will assume that they have a good system (if they have a lot of losses as well it means the system isn't perfect, but it still works), but if you only win once and never enter again I assume it is survivorship basis. You're sampling M&Ms out of the freakin' peanut M&M production line. If you fix your mistake, you'll get all kinds of M&Ms. You do not fix your mistake with higher sampling. Your mistake is that you're unwittingly filtering out an important subset of the problem domain, and proceeded to do a faulty analysis on the subset you picked.

> (...) and they keep winning That's the textbook definition of survivorship bias. It's like a lottery winner boasting about his winning strategy that everyone else is just not able to learn.

bumby 2y ago

I read the OP differently. I think there can be many angles to a person's identity makeup and they don't always cohere perfectly. A person can have their identity in "entrepreneurial libertarian" while also having it as "someone who comes from a family valuing military service". Humans aren't always perfectly rational when it comes to their different values/tribal identities. There are a lot of people who join the military while simultaneously "hate authority" for example.

chipdart 2y ago
My answer was not touching on the topic of identity. I'm referring to the relationship between cause ad effect.
- bumby 2y ago
  That is still downstream of the question of “why would someone make such a [seemingly contradictory] decision.” The person you replied to is misunderstanding the OP, which they later clarified. My point is understanding outcome causality doesn’t necessarily have to even enter the decision.

bluGill 2y ago

At what point do we conclude the lottery isn't as random as they claim if one person keeps winning. Statistically someone will win, there are good odds someone will win twice, but the odds of anyone winning 3 times is almost zero.

chipdart 2y ago
> At what point do we conclude the lottery isn't as random as they claim if one person keeps winning. The whole thing about survivorship bias is that you make a critical failure in analysis when confusing partial observations of post-facto results with causality.
- bluGill 2y ago
  The point of statistics (one of many) is to figure out how many observations we need. If someone wins the lottery 10 times with their system I will assume that they have a good system (if they have a lot of losses as well it means the system isn't perfect, but it still works), but if you only win once and never enter again I assume it is survivorship basis. Of course by winning the lottery I mean win a large jackpot - most have smaller prizes that you have high odds of winning many times if you play often enough.
  - manwe150 2y ago
    Only if you only play 3 times though (in your previous example). Statistics also are about figuring out what sort of outliers must exist for a process to be fair (true random). For something like a mega lottery with terrible odds, then winning twice is already very unlikely. But for something easy like a coin flip, every N trials should have a run of about sqrt N heads or wins in a row if it is unbiased. For something unlikely like lotto, it is closer to looking at the birthday paradox: the probability of one person winning twice is low; but the probability that there exists a person who won twice is high, at random.
  - chipdart 2y ago
    > The point of statistics (one of many) is to figure out how many observations we need. No, you're missing the whole point. Think about the problem about survivorship bias. Imagine you are at a M&Ms factory. You decide you want to assess what's the color distribution of M&Ms by sampling the colors that come out of the production line. You somehow make the mistake of sampling the production line for the peanut core M&Ms right out of the pipe that produces yellow M&Ms. You sample away and after hours you present your findings: 99.9% of yellow M&Ms have a peanut core. Based on your findings, you proceed to boldly claim that having a yellow core is a critical factor in producing yellow M&Ms. You even go as far as to rationalize it, and claim that yellow represents peanuts, and if anyone wants to create yellw-colored candy they need to start by adding peanut to the mix. I then alert you to the fact that you made a critical failure in analysis when confusing partial observations of post-facto results with causality. Your answer: > The point of statistics (one of many) is to figure out how many observations we need. If someone wins the lottery 10 times with their system I will assume that they have a good system (if they have a lot of losses as well it means the system isn't perfect, but it still works), but if you only win once and never enter again I assume it is survivorship basis. You're sampling M&Ms out of the freakin' peanut M&M production line. If you fix your mistake, you'll get all kinds of M&Ms. You do not fix your mistake with higher sampling. Your mistake is that you're unwittingly filtering out an important subset of the problem domain, and proceeded to do a faulty analysis on the subset you picked.