P-value fallacy
Let's say you have a group of friends, and you want to see if they are taller than the average person. So you measure all their heights, and you find that their average height is 5 feet, 7 inches.
But how do you know if this is just because of chance, or if it's because they are actually taller than the average person? This is where something called the p-value comes in.
The p-value is like a percentage that tells you how likely it is that your result (in this case, the average height of your friends) could have occurred by chance. If the p-value is very low (like less than 0.05), then you can say that it's pretty unlikely that your result occurred by chance, and you can say that your friends are probably taller than the average person.
But here's where the fallacy comes in: just because the p-value is low, it doesn't mean that your result is definitely true. There could still be other factors that you didn't take into account.
For example, maybe your friends all play basketball, and basketball players tend to be taller than average. So even if the p-value says that your friends are likely taller than average, you can't say for sure that it's because they are inherently taller -- it could be just because they all play basketball.
So the p-value fallacy is when people rely too much on the p-value as a definitive answer, without considering other factors that might contribute to the result.
But how do you know if this is just because of chance, or if it's because they are actually taller than the average person? This is where something called the p-value comes in.
The p-value is like a percentage that tells you how likely it is that your result (in this case, the average height of your friends) could have occurred by chance. If the p-value is very low (like less than 0.05), then you can say that it's pretty unlikely that your result occurred by chance, and you can say that your friends are probably taller than the average person.
But here's where the fallacy comes in: just because the p-value is low, it doesn't mean that your result is definitely true. There could still be other factors that you didn't take into account.
For example, maybe your friends all play basketball, and basketball players tend to be taller than average. So even if the p-value says that your friends are likely taller than average, you can't say for sure that it's because they are inherently taller -- it could be just because they all play basketball.
So the p-value fallacy is when people rely too much on the p-value as a definitive answer, without considering other factors that might contribute to the result.
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