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Floats tend to be overused by programmers. Almost every real-world quantity (time, money, sensor readings...) is actually quantized, and is better represented via integers (ms, cents, etc.) The only real use case of floats is scientific computing (physics, ML, simulations...)
There are significant issues with floats. To start with, float precision varies based on the number considered. The typical distance between consecutive two large floats is much larger than the typical distance between two floats close to 0. This can lead to surprising behavior.
And importantly, floats can't accurately represent base 10 numbers used for large quantities, like money. A 10e10 float is actually smaller than 10e10, for instance. Even 0.7 float is actually smaller than 0.7.
This means floats lead to weird numerical discrepancies that accumulate as you add more operations. Only use floats if you want to run large computation with approximate numbers -- scientific computing, basically.
I argue even for physics! I used int64 for units in the physics library I wrote for periph. A challenge is that it is less intuitive for users with less programming experience. A drawback is that on Intel x64, division is faster in float than int64 (!) https://periph.io/x/conn/v3/physic …
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