Precision errors are common in competitive programming, particularly when using floating-point numbers in calculations. These errors arise because floating-point representations cannot store all decimal values precisely, leading to rounding issues in results. To minimize precision errors, consider using integer arithmetic where feasible, as it avoids the need for rounding. If floating-point calculations are essential, rounding functions or setting precision limits, as some languages offer, can help manage inaccuracies. For tasks requiring precise comparisons, using epsilon values—a very small tolerance range—can aid in comparing two numbers effectively. Understanding and applying these techniques allows programmers to handle precision-dependent problems more accurately, avoiding unexpected results due to tiny decimal discrepancies.