Loss of precision

Loss of precision occurs when numeric operations or conversions lead to inaccuracies, often because floating-point or integer types cannot exactly represent certain numbers or decimals. For instance, storing or converting decimal values to floating-point representations can introduce small rounding errors, leading to unexpected or incorrect calculations. Similarly, converting large integers into smaller numeric types or between incompatible data types can truncate significant digits or alter numeric values. These inaccuracies can accumulate, significantly impacting calculations, financial operations, or precise computations.

To prevent loss of precision, choose numeric data types appropriate for the required precision and scale—such as decimal or BigInt types for financial and large integer calculations. Avoid unnecessary type conversions, and perform explicit rounding or truncation carefully to maintain accuracy. When working with floating-point arithmetic, consider using specialized numeric libraries designed for high-precision arithmetic. Regularly validate numeric operations, especially during conversions, and apply rigorous testing to detect potential precision-related issues early in development.