Floating Point Math Your language isn’t broken, it’s doing floating point math. Computers can only natively store integers, so they need some way of representing decimal numbers. This representation is not perfectly accurate. This is why, more often than not, 0.1 + 0.2 != 0.3 . Why does this happen? It’s actually rather interesting. When you have a base-10 system (like ours), it can only express fractions that use a prime factor of the base. The prime factors of 10 are 2 and 5. So 1/2, 1/4, 1/5, 1/8, and 1/10 can all be expressed cleanly because the denominators all use prime factors of 10. In contrast, 1/3, 1/6, 1/7 and 1/9 are all repeating decimals because their denominators use a prime factor of 3 or 7. In binary (or base-2), the only prime factor is 2, so you can only cleanly express fractions whose denominator has only 2 as a prime factor. In binary, 1/2, 1/4, 1/8 would all be expressed cleanly as decimals, while 1/5 or 1/10 would be repeating decimals. So 0.1 and 0.2...
