After death of Joshua Dean & John Barnett, their lawyers are concerned about the possibility that around 10 more Boeing whistleblowers may suffer the same fate.
You don’t compare the stats to the population in its entirety. That’s like trying to calculate how dangerous it is for cyclists on the road by using the entire population. Most Americans don’t even own a bike. It is literally impossible for them to die riding their bike to work because they never ride a bike to work. They should not be included in an assessment of how dangerous it is/isn’t to be a daily bike commuter. Only people who ride bikes regularly should be included.
An example I use in another comment: you are far more likely to die falling to your death if you skydive once a year than the average American. If you suddenly died due to a parachute malfunction nobody would immediately start citing how statistically unusual it is for an American to die from falling to their death. You would be compared against the larger skydiving population and other risk factors would be assessed with that.
No, the whole point is you don’t unless you’re specifically asking “how likely is it for anyone” which is not applicable here. That’s like me trying to figure out how likely I am to die of diabetes within 24hrs when I don’t have diabetes. The answer is 0%, because I don’t have diabetes. So including me in a stat about likelihood of death when discussing diabetes is bad math unless you are trying to calculate the likelihood of it happening to literally anyone, which is not useful beyond answering that specific question.
You’re also denying the existence of more at-risk groups for things like suicide and illnesses. Different groups are more at risk than others. Imagine calculating how likely someone is to be an alcoholic without considering family history, their social and economic realities, etc. all of which increase or decrease the % chance they will develop alcoholism. Literally 0 experts will agree with your assessment if you leave those risk factors out.
You don’t include people who aren’t at risk of MRSA. You and I right now discussing this have a near-0% chance of catching it. So near that it functionally is 0%. We are not useful information. We should not be included in calculating the probability because he is more at risk by a large margin. We are not part of the data set.
From what is currently known about the two whistleblowers neither were particularly at higher risk of suicide or MRSA. The person who died of MRSA was healthy and active with no history of hospitalization whatsoever. Close friends of the first whistleblower claim that suicide was very unlike him, and his previous statement of “if anything happens, it wasn’t suicide” strengthens that.
There are other commenters here speculating that being a whistleblower makes you at higher risk of suicide, but there are no official statistics on that, so it is at most speculation, therefore I need to use general statistics.
All probabilistic models and datasets eventually get replaced with more accurate ones, but that doesn’t discredit them until then.
You don’t compare the stats to the population in its entirety. That’s like trying to calculate how dangerous it is for cyclists on the road by using the entire population. Most Americans don’t even own a bike. It is literally impossible for them to die riding their bike to work because they never ride a bike to work. They should not be included in an assessment of how dangerous it is/isn’t to be a daily bike commuter. Only people who ride bikes regularly should be included.
An example I use in another comment: you are far more likely to die falling to your death if you skydive once a year than the average American. If you suddenly died due to a parachute malfunction nobody would immediately start citing how statistically unusual it is for an American to die from falling to their death. You would be compared against the larger skydiving population and other risk factors would be assessed with that.
You do for disease and suicide as it can happen to literally anyone.
If working for a specific company or being a whistleblower affects those statistics, the company should be held responsible anyway.
No, the whole point is you don’t unless you’re specifically asking “how likely is it for anyone” which is not applicable here. That’s like me trying to figure out how likely I am to die of diabetes within 24hrs when I don’t have diabetes. The answer is 0%, because I don’t have diabetes. So including me in a stat about likelihood of death when discussing diabetes is bad math unless you are trying to calculate the likelihood of it happening to literally anyone, which is not useful beyond answering that specific question.
You’re also denying the existence of more at-risk groups for things like suicide and illnesses. Different groups are more at risk than others. Imagine calculating how likely someone is to be an alcoholic without considering family history, their social and economic realities, etc. all of which increase or decrease the % chance they will develop alcoholism. Literally 0 experts will agree with your assessment if you leave those risk factors out.
You don’t include people who aren’t at risk of MRSA. You and I right now discussing this have a near-0% chance of catching it. So near that it functionally is 0%. We are not useful information. We should not be included in calculating the probability because he is more at risk by a large margin. We are not part of the data set.
From what is currently known about the two whistleblowers neither were particularly at higher risk of suicide or MRSA. The person who died of MRSA was healthy and active with no history of hospitalization whatsoever. Close friends of the first whistleblower claim that suicide was very unlike him, and his previous statement of “if anything happens, it wasn’t suicide” strengthens that.
There are other commenters here speculating that being a whistleblower makes you at higher risk of suicide, but there are no official statistics on that, so it is at most speculation, therefore I need to use general statistics.
All probabilistic models and datasets eventually get replaced with more accurate ones, but that doesn’t discredit them until then.