I heard this statement and it confused me. The explanation was more red cars have accidents than other cars. But surely that doesn’t translate to “I personally am more likely to have an accident if I drive a red car than a blue car today”? Assuming there’s nothing inherently about red cars that makes them more likely to crash. I’m struggling with the maths theory behind it.
Edit to clarify my question: does the statistic that “red cars have more accidents” translate to the statement that “I, personally, all other things being equal, am more likely to have an accident if I drive a red car than a blue one”?
In: 10
This is not exactly math but is related to formal logic, and specifically a phenomena that is very important in science and politics. It is important to distinguish correlation and causality. Correlation is when two variables have some sort of relation. In your case it is the color of the car and the chance of getting into an accident. Causality is when one thing causes something else. You are reading this correlation as if the color of the car causes the car to be involved in an accident, but this is not always the case. Correlation does not mean there is a causality. It could easily be that people who buy red cars are the same type of people who are often involved in traffic accidents. But the statement does not specify if this is the case or not.
There are several ways of finding causality once you have found a correlation. You could collect more data to try to find more correlations, for example if you find out that a certain population group are correlated with both red cars and traffic accidents even ignoring the red cars that are in traffic accidents then you have a causality. Or you could try to manipulate one of the variables, for example would banning red cars reduce traffic accidents? If not then there is no causality.
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