There's a point beyond which you can find correlations that have nothing to do with cause and effect. You need enough ways to check against that to remove those correlations from the analysis. Things like the number of lows of bread eaten in each year in Denmark is incredibly well correlated with whether the u.s stock market goes up or down somethingyou know they're just craziness. And as truck said it really is a matter of you know they're they're words for it so you have one set of data it's called the training data and that's the data you use to build your model, then you have this other set of data which is called the validation data which is the data that you

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