Floating-point precision errors occur because some numbers can’t be represented exactly in binary, leading to small inaccuracies. In competitive programming, these tiny errors can cause incorrect results when comparing floating-point numbers. To avoid these issues, use an epsilon value when comparing two floating-point numbers to check if they are ‘close enough’ rather than exactly equal. Alternatively, if the problem allows, switch to integer-based calculations, which are more precise. For example, if dealing with percentages, scale up the values to integers to avoid fractional numbers. Handling floating-point numbers carefully is key to avoiding precision-related bugs in your solutions.