It's maybe some kind of philosophical question. I have a large dataset with information about customers. I return to the dataset from time to time to make some estimations or check different hypothesis.
The longer I look into the data the more probable I will find some spurious insights there. However if I can't know in advance all ideas that will come in my mind and it's hard to track all analysis I and others do with this data. So how can I address type I errors? The paper "Towards sustainable insights" warns that if I haven't checked hypothesis with formal test but made some decision eyeballing the density plots I've already made multiple comparisons. Life is pain.
But should I worry? The Iris dataset is analysed by thousands of people for decades. I believe they already tested everything possible about it. Although it doesn't imply that I mush divide my alpha-level by millions when comparing petal lengths.