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a new sample of data, therefore bearing little use. This is sometimes caused by investigating too many hypotheses and not performing proper statistical hypothesis testing. A simple version of this problem in machine learning is known as overfitting, but the same problem can arise at different phases of the process and thus a train/test split--when applicable at all--may not be sufficient to prevent this from happening.

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