Correlation is just two data points that seem to match up.
Say you have data on shark attacks, and data on temperature. When looking at the data side by side, you see that as temperature increases, it seems shark attacks do as well. This is called a correlation.
Causation would be saying that increased temperature is what causes sharks to be more agressive based solely on that correl
This may b true, but it may not (i dont actually know, it was just a random example). Correlation does not take into account any other factors. In this case, the shark attacks are likely just because there are more people at the beach when its hot, not that the temp actually affects the sharks in any way. Since the Correlation is there, but it can’t confirm the causation, the idea of Correlation does not equal causation came to be.
However, this does not mean correlations should be dismissed as many people who use that phrase imply, it simply means you need more evidence than just the Correlation to accurately claim causation.
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