The Bad Hair, Incorrect Feathering, and Missing Skin Flaps of Dinosaur Art

Reptiles and some dinosaurs have a sprawling, splayed-out posture. Other dinosaurs, birds, and mammals have independently-evolved upright postures. The birds apparently evolved from the splayed-hip dinosaurs rather than from the upright-hip dinosaurs, so a change in hip structure could be a decent dividing line.

But strictly speaking, birds (Aves) have wings as their forelimbs, rather than arms. The (extinct) moa is an exception in that it had no wings, but that is because it had no forelimbs at all.

It is very likely that other theropods besides the birds also had feathers, but it is rare for soft tissues to fossilize, so it can be hard to say what the scale and feather coverage was like for a given species when all you have is a partial skeleton. Determining coloration is almost impossible, though it has been estimated from especially well-preserved fossilized impressions of feathers that showed different spacing between the microstructures on the feather.

Now that we know many dinosaurs are most closely related to birds it is causing a revolution in dinosaur art.

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.

If the information is not relevant but mistakenly assumed to be, because (e.g.) it is not, but treated as, representative of the class of animals to which it is applied, it can produce a worse model than one constructed without the bad information.

By including information that is not relevant, or that is redundant.

Search online for Feature Subset Selection. It's a thing. Given that more information makes training more expensive, if a lot of your data does not contribute to your model then you have a very good reason to exclude it. Conversely, you have a motive to not include irrelevant information.