: p-value in statistics
In: 10
The p-value is basically a measure of how likely two different groups of data are actually different.
Let’s do a sports example. Player A shoots 10 free throws and makes 4. Player B shoots 10 free throws and makes 5.
Now, we could argue that Player A is better than Player B. However, given the small amount of free throws, it’s entirely possible that they’re equally as good. If I asked them to take another 10 free throws, A might make 6 and B might make 4.
When analyzing Player A’s free throws and Player B’s free throws, we want to estimate the likelihood that they’re equally talented. This is the p-value. The p-value is the likelihood that both players are equally skilled. So if you see a p-value of 0.6, that means there’s ~60% chance they’re equally skilled.
Now, this is an oversimplification. The exact definitions from a mathematical standpoint are different and more specific. But this example illustrates the general idea.
EDIT: As u/banana_stand_savings has indicated, there becomes some issues with my explanation when you’re actually doing research. P-values are often misinterpreted, and my explanation isn’t an exact definition. [Here is the Wikipedia page on the topic.](https://en.wikipedia.org/wiki/Misuse_of_p-values)
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