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If I have a set of data and I am not sure about the data at all or the meaning I would try to investigate the dataset a bit first. I understand the importance of having a training-validation-test split when it comes to evaluating model performance but should this also be applied before when I am performing EDA for example?

I assume no, because I want to know that the data in my respective splits is correct, doesn't contain errors etc. Is there a point however when I am performing these checks, i.e. distribtion checks of features etc. that I can inadverntanly include bias into my model by not having the split until the data is prepared?

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I do a basic EDA before the split. Otherwise you wouldn't know if there are any errors in the data. Say missing values or typos. Also for outlier detection.

Furthermore, and EDA before the split makes it possible for a stratification if needed otherwise there might be an over representation in the training data that doesn't replicate to other data.

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