> I think that must be a fallacy, but I'm not sure which. How can we do worse by including more information? I don't think the term "fallacy" applies either wa

> I think that must be a fallacy, but I'm not sure which. How can we do worse by including more information? I don't think the term "fallacy" applies either way because the parent's point not reducible to a logical error. It's more a matter of perspective. Theoretically speaking, more information is always better. But that's only if you can feasibly work with arbitrary amounts of data. For example, in a deterministic universe we could simulate every instance of time forwards and backwards throughout history, but by definition we'd require more computing power than is possible to perfectly simulate our universe. Similarly, if your information far outstrips your computational capabilities, you can no longer meaningfully work with the data. There are pretty hard economic limits on what kind of data we can work with today, so practically speaking it's accurate to say that more information can be a net negative. In particular, a preoccupation with acquiring more data can lead the analysis to focusing on the wrong data, or it can lead to a hopelessly noisy analysis, or it can lead to subjective decisions about which data to use that meaningfully alters the conclusions. Mathematically speaking, basic results from combinatorics (e.g. Ramsey theory) guarantee that at a sufficiently great size, your data is guaranteed to contain spurious correlations that have to be controlled for. Whenever I'm working on an applied data analysis project I try to use as little information as I can from the outset. Sometimes this means fewer dimensions of data points, or smaller slices of a timeseries. But either way it results in less overall information to process.