When converting a string representation of a number to a double-precision floating-point number, precision loss can occur due to fundamental differences between the binary and decimal numeral systems. Doubles are stored using binary fractions, which cannot precisely represent all decimal values, leading to potential inaccuracies during conversion. For instance, the string “0.1” converts to an approximate value in binary, resulting in a small rounding error when converted back to a string. However, numbers like “0.5” convert without loss since they are finite in binary form. To address this issue, consider using decimal data types for precise financial calculations or implementing rounding methods based on the original string’s precision.