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Maybe they're not universal, but each is a strong attractor of models given a relatively small number of commonly satisfied assumptions.

In particular: dimensioned measurements must represent the same relationships between data under many different scales thus leading to logarithmic sampling.

Without further information to give expectations to other trends, these laws great starts.

If by must you mean often tend to, and by relationships you mean ratios, then I agree; it's a very useful model in the absence of other information. Wonderful, in fact.

Still, sociologically, working scientists tend to believe in these models as if they were hard rules, to the extent of constructing bridges, rockets, nuclear reactors, nationwide health recommendations and global financial systems without fundamentally understanding why each distribution might arise, and why it might fail to explain real phenomena.


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