In the US, the CDC collects data on lightning strike related injuries. Take number of people struck by lightning in a year divided by number of people in the population, there are your odds.
I’ve been thrown back by lightning (ground wave). So count how many comments in this conversation come from people who have been hit (like me), and divide by the total number of comments.
Let’s assume that you know the following information:
When each strike happened, who it happened to, how old the person was when it happened, how long they lived overall/had lived overall by that point, population sizes, when any deaths occurred, and when any births occured.
First off, are we assuming that every person has an equal chance of getting struck?
If we assume nobody is more prone to being struck than any other person and therefore each strike is one that may possibly hit you, then every instance of lighting striking a person counts as a “point” (points = number of rows).
If we are assuming that there isn’t necessarily an equal chance, and if someone gets struck multiple times then maybe they just happen to be a lightning-attracting person or something, then the chance is more represented by the proportion of people struck. Here, each different person who’s struck counts for a “point” (points = number of different people). I’ll discuss why you might choose which later.
Next, what are your general parameters? Chance per person year? For a time period? Chance per lifetime?
Your general formula is
(Number of points)
Divided by
(The number of options to get a point)
You may assume that the number of options to get a point would just be the population size, but it would more truthfully be the amount of people existing during the time period. So if you have a population of three people, one of which was alive for the whole period, one of which was born 1/4th the way through (existed for 3/4th the time), and one of which died 3/5th the way through (existed 3/5th the time), then your “number of options to get a point would be 1+3/4+3/5 or 1.35, as essentially 1.35 people existed during that period.
Now, let’s say you want to know the chance someone in your subject population gets struck by lightning during some time period. Let’s pretend that the period is one year, and during that year there were 6 instances of a person being struck by lightning and during this year the population had 600 total people with no births or deaths.
If we assume that each person had equal chance of being struck, the chances of getting stuck by lightning that year for anyone in that population is 6 out of 600, or 1%.
Okay now let’s say that instead of a year, this was for a lifetime, wherein each individual had died after 100 years. So 600 people each living 100 years (which combines to a total of 60,000 years lived). So the chance of getting struck in a lifetime is 1% and the chance of getting struck per year is 0.01%.
But not everyone lives the same length of life, nor has everyone died. You’ll just add the years up. So lets say that those 600 people had altogether lived a total of 50,700 combined years. If there have been 6 strikes so far for all those years lived, then the the chance of getting struck per year is 6/50,700, or about 0.0118%. So if someone in that population lived 90 years, then we can multiply by 90 to find that they had a 1.062% chance of getting struck in their lifetime.
Now let’s go back to considering 6 strikes happening within one year for a population of 600. Let’s say that of those 6 strikes, one person was struck twice, one person was struck three times, and one person was struck once. So in total, 3 people were struck. Would this impact your calculations?
Think about rolling a die. Each time you roll a die, you have 1/6th chance of rolling any number. Pretend you rolled one. If you roll the dice a second time, only 1 of six sides is a one, so you now have 1/6th a chance of rolling the same number. You roll a one again. The chance of getting a one on your next roll is still 1/6, but the total chance of getting it three times in a row multiplies the individual chances, so it’s 1/6•1/6, or 1/36 chances you roll the same number 3 times in a row. Which can certainly happen, but it’s not very likely. So when in our prior method where we assume that each person constantly has a 1% chance of being struck, the people who got stuck more than once just had some uniquely really really bad luck. Essentially, it just so happened that the same person rolled the same number and others didn’t, but the number could have been rolled by anyone.
But what if the dice you’re using is weighted in favor of rolling a one? Then the chances of rolling a one multiple times in a row would be a lot higher! So, what if the people who were struck multiple times had some weird traits that attracted lightning to them? Now we don’t know for sure, so it’s really up to you whether or not this impacts your calculations.
Let’s assume that there is in fact a bias and that being stuck by lightning is a result of some inherent trait and not just random. So, anyone who’s struck by lightning multiple times just exhibits that trait more strongly and we are essentially more interested in identifying how likely it is any one person has this trait to degree strong enough that they will get hit at all. Now our estimates by going based on the number of people struck instead. So 3/600 equals 0.5% chance of being a person who gets struck that year in that population during that time period.
But realistically we can’t really say that this trait does or doesn’t exist and can’t really identify it if so or say that it’s the direct causation of every strike ever. This is where things get more complicated.
In most basic estimates you’ll do one of these two methods. For example, a die can be tested to see if its weighted and if it’s not then it’s fair to assume that any repeats are just chance. Meanwhile, if you want to know the likelihood of an employee getting MRSA in a typical office workplace, you might go based on the number of people who’ve been infected instead of number of infections that have occurred because it’s common for MRSA to reappear multiple times once someone’s had it.
If we want to test to see if the people in our population are getting stuck abnormally often, we can compare the distribution of our strikes per person data to the strikes per person data that exists for the larger population (ex. State vs country). We can also identify and avoid considering any outliers by using the average and standard deviation to do some fancy statistics stuff. Basically, fancy statistics shit allows for us to be able to identify if there is likely to be a bias and allows us to make our likelihood more proportional by applying that bias in some way. I’m studying fisheries biology and we do a lot of statistical work on R studio. It’d be an absolute beast to do this sorta thing by hand, which is partially why it’s not super easy to explain.
In regards to lightning strikes, the easiest way to get a more accurate risk assessment would be going based off of factors that are likely to increase exposure to lighting. Someone who lives in an area that has more frequent lighting is more likely to be struck. Someone who spends a lot of time outside in fields is also more likely to be struck. This is why it may be helpful to divide data up into certain populations.
Essentially, any “changes of you getting struck by lightning” statistic you see is making a lot of assumptions and isn’t necessarily accurate, nor may it be consistent across sources because there are different ways to come to this predicted chance.
Anyway I’m too tired to read through this so if I have any minor spelling or math errors uhhhh just lmk lol
Lightning strikes are recorded by weather stations, so you can track how many happen during any given storm in any given area. Lets say you’re looking at “how likely am I to be struck by lightning in Kansas” as our example. Take all the lightning strikes in a year in Kansas, and how many people got struck by lightning in Kansas. Add years and the lightning strikes within those years until you get to somebody getting hit, that’s your chances.
Comments
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In the US, the CDC collects data on lightning strike related injuries. Take number of people struck by lightning in a year divided by number of people in the population, there are your odds.
Estimate how many people have been struck by lightning compared to how often lightning strikes without hitting someone.
Estimate number of people in world
Estimate number of times people are hit by lightning
Both figures get widely reported, so you can figure out how many people get struck by lightning compared to how many people there are
The question is where. At Lake Maracaibo in Venezuela, lightning strikes hundreds of times every hour.
For you, you look up statistics of people getting struck by lightning in your county or state. In general, it’s about 1 in 1 million people per year.
I’ve been thrown back by lightning (ground wave). So count how many comments in this conversation come from people who have been hit (like me), and divide by the total number of comments.
Actually kinda complicated.
Let’s assume that you know the following information:
When each strike happened, who it happened to, how old the person was when it happened, how long they lived overall/had lived overall by that point, population sizes, when any deaths occurred, and when any births occured.
First off, are we assuming that every person has an equal chance of getting struck?
If we assume nobody is more prone to being struck than any other person and therefore each strike is one that may possibly hit you, then every instance of lighting striking a person counts as a “point” (points = number of rows).
If we are assuming that there isn’t necessarily an equal chance, and if someone gets struck multiple times then maybe they just happen to be a lightning-attracting person or something, then the chance is more represented by the proportion of people struck. Here, each different person who’s struck counts for a “point” (points = number of different people). I’ll discuss why you might choose which later.
Next, what are your general parameters? Chance per person year? For a time period? Chance per lifetime?
Your general formula is
(Number of points)
Divided by
(The number of options to get a point)
You may assume that the number of options to get a point would just be the population size, but it would more truthfully be the amount of people existing during the time period. So if you have a population of three people, one of which was alive for the whole period, one of which was born 1/4th the way through (existed for 3/4th the time), and one of which died 3/5th the way through (existed 3/5th the time), then your “number of options to get a point would be 1+3/4+3/5 or 1.35, as essentially 1.35 people existed during that period.
Now, let’s say you want to know the chance someone in your subject population gets struck by lightning during some time period. Let’s pretend that the period is one year, and during that year there were 6 instances of a person being struck by lightning and during this year the population had 600 total people with no births or deaths.
If we assume that each person had equal chance of being struck, the chances of getting stuck by lightning that year for anyone in that population is 6 out of 600, or 1%.
Okay now let’s say that instead of a year, this was for a lifetime, wherein each individual had died after 100 years. So 600 people each living 100 years (which combines to a total of 60,000 years lived). So the chance of getting struck in a lifetime is 1% and the chance of getting struck per year is 0.01%.
But not everyone lives the same length of life, nor has everyone died. You’ll just add the years up. So lets say that those 600 people had altogether lived a total of 50,700 combined years. If there have been 6 strikes so far for all those years lived, then the the chance of getting struck per year is 6/50,700, or about 0.0118%. So if someone in that population lived 90 years, then we can multiply by 90 to find that they had a 1.062% chance of getting struck in their lifetime.
Now let’s go back to considering 6 strikes happening within one year for a population of 600. Let’s say that of those 6 strikes, one person was struck twice, one person was struck three times, and one person was struck once. So in total, 3 people were struck. Would this impact your calculations?
Think about rolling a die. Each time you roll a die, you have 1/6th chance of rolling any number. Pretend you rolled one. If you roll the dice a second time, only 1 of six sides is a one, so you now have 1/6th a chance of rolling the same number. You roll a one again. The chance of getting a one on your next roll is still 1/6, but the total chance of getting it three times in a row multiplies the individual chances, so it’s 1/6•1/6, or 1/36 chances you roll the same number 3 times in a row. Which can certainly happen, but it’s not very likely. So when in our prior method where we assume that each person constantly has a 1% chance of being struck, the people who got stuck more than once just had some uniquely really really bad luck. Essentially, it just so happened that the same person rolled the same number and others didn’t, but the number could have been rolled by anyone.
But what if the dice you’re using is weighted in favor of rolling a one? Then the chances of rolling a one multiple times in a row would be a lot higher! So, what if the people who were struck multiple times had some weird traits that attracted lightning to them? Now we don’t know for sure, so it’s really up to you whether or not this impacts your calculations.
Let’s assume that there is in fact a bias and that being stuck by lightning is a result of some inherent trait and not just random. So, anyone who’s struck by lightning multiple times just exhibits that trait more strongly and we are essentially more interested in identifying how likely it is any one person has this trait to degree strong enough that they will get hit at all. Now our estimates by going based on the number of people struck instead. So 3/600 equals 0.5% chance of being a person who gets struck that year in that population during that time period.
But realistically we can’t really say that this trait does or doesn’t exist and can’t really identify it if so or say that it’s the direct causation of every strike ever. This is where things get more complicated.
In most basic estimates you’ll do one of these two methods. For example, a die can be tested to see if its weighted and if it’s not then it’s fair to assume that any repeats are just chance. Meanwhile, if you want to know the likelihood of an employee getting MRSA in a typical office workplace, you might go based on the number of people who’ve been infected instead of number of infections that have occurred because it’s common for MRSA to reappear multiple times once someone’s had it.
If we want to test to see if the people in our population are getting stuck abnormally often, we can compare the distribution of our strikes per person data to the strikes per person data that exists for the larger population (ex. State vs country). We can also identify and avoid considering any outliers by using the average and standard deviation to do some fancy statistics stuff. Basically, fancy statistics shit allows for us to be able to identify if there is likely to be a bias and allows us to make our likelihood more proportional by applying that bias in some way. I’m studying fisheries biology and we do a lot of statistical work on R studio. It’d be an absolute beast to do this sorta thing by hand, which is partially why it’s not super easy to explain.
In regards to lightning strikes, the easiest way to get a more accurate risk assessment would be going based off of factors that are likely to increase exposure to lighting. Someone who lives in an area that has more frequent lighting is more likely to be struck. Someone who spends a lot of time outside in fields is also more likely to be struck. This is why it may be helpful to divide data up into certain populations.
Essentially, any “changes of you getting struck by lightning” statistic you see is making a lot of assumptions and isn’t necessarily accurate, nor may it be consistent across sources because there are different ways to come to this predicted chance.
Anyway I’m too tired to read through this so if I have any minor spelling or math errors uhhhh just lmk lol
You shouldn’t ask such questions, big lighting will strike you DOWN!
maybe it is just me but I’d prefer to not get hit by lightning, so I’d call it a risk not a chance
Lightning strikes are recorded by weather stations, so you can track how many happen during any given storm in any given area. Lets say you’re looking at “how likely am I to be struck by lightning in Kansas” as our example. Take all the lightning strikes in a year in Kansas, and how many people got struck by lightning in Kansas. Add years and the lightning strikes within those years until you get to somebody getting hit, that’s your chances.