Because value !== 0.3 and that can cause unexpected problems on the edges (which are the worst kind of issues).

I'm far from wizard on the topic, but from my experience it's not that simple since the relative magnitudes of the operands and the operator affect the size of the error in non obvious ways.

I spent 13 years maintaining a 2mloc reservation and accounting system that used floats for everything. By the time I got there and understood enough it was simply too late to do anything about it.

Just say no, really; that road leads to madness.

If you need precision; and many do; consider fixnums, rationals or bignums, in order of increasing complexity and flexibility.

I might suggest reading https://doc.lagout.org/science/0_Computer%20Science/3_Theory...

This is in general how floating point math works.

This is actually the plot of a few movies, people have been convicted of it in real life. [1]

[1] https://en.wikipedia.org/wiki/Salami_slicing

I ran into this once because a price was slightly too low, and got truncated. A $19.99 item got displayed as $19.98. You can work around this by rounding everywhere but that’s error-prone.

How much of an issue is it?

The kind of issue do not want to have: issues with money that can go very wrong when calculating discounts, tax percentages, interest or whatever.

After doing a lot of calculations? This is a big problem.

The firm I worked at used built in Java libraries and always deferred rounding until the very last possible moment.