Floating-point precision errors arise due to the limited precision of floating-point representations. Use fixed-point arithmetic or work with integers.

Floating-point numbers are prone to precision errors because of how they are represented in computers. For example, numbers like 0.1 cannot be represented exactly in binary, leading to small rounding errors that can accumulate over multiple operations. In competitive programming, these errors can cause wrong answers, especially when exact comparisons are needed. To avoid this, use fixed-point arithmetic if possible, or represent values as integers to eliminate rounding errors. For example, instead of working with percentages, multiply all values by 100 and work with integers. When floating-point numbers are necessary, use an epsilon value to compare numbers within a small range of precision. Handling floating-point numbers carefully ensures more accurate solutions.