To calculate a percentage of a price, you need a data type that can handle decimal values, as the result may not be a whole number. Here's why a floating-point data type is appropriate:
1.Precision: Floating-point data types are designed to handle numbers that have a fractional part, which is essential for representing percentages accurately.
2.Calculation: When calculating 90% of a retail price, you multiply the price by 0.9. This operation requires a data type that can represent both the price and the resulting product with decimal precision.
3.Storage: Floating-point types can store a wide range of values with precision, making them suitable for financial calculations where the exact amount matters.
The other options are not suitable for the following reasons:
*A. Boolean: Can only represent two values, true or false.
*C. Auto-number: Typically used for unique identifiers and cannot represent fractional values.
*D. String: While it can represent a number as text, it is not suitable for calculations without conversion, which is inefficient and error-prone.
References: The decision to use a floating-point data type for percentage calculations is supported by best practices in programming and database design, as discussed in various programming forums and documentation123. These sources recommend using decimal or floating-point types for financial calculations to ensure accuracy and avoid errors that can arise from binary floating-point representations2.