Range, Precision, and Storage Capacity
TLDR: Range, precision, and storage capacity are fundamental attributes of data types in computing, defining the limits, accuracy, and memory allocation for representing values. Range specifies the minimum and maximum values a type can hold, precision determines the level of detail or significant digits it can represent, and storage capacity refers to the amount of memory used by the data type. Together, these characteristics influence the suitability of data types for specific computational tasks.
https://en.wikipedia.org/wiki/Data_type
The range of a data type is directly tied to its storage capacity and representation model, such as signed or unsigned integers and floating-point formats. For example, in Java, a 32-bit `int` has a range of -2,147,483,648 to 2,147,483,647, while an unsigned 32-bit integer extends from 0 to 4,294,967,295. Floating-point types, defined by the IEEE 754 standard, provide vast ranges but with trade-offs in precision. A single-precision float (32 bits) can represent approximately ±3.4 × 10^38, whereas double-precision floats (64 bits) extend to ±1.8 × 10^308.
https://standards.ieee.org/standard/754-2019.html
Precision is critical in applications where exact values matter, such as scientific calculations or financial modeling. Data types like BigDecimal in Java or NumPy's `float128` in Python offer higher precision at the cost of increased storage capacity. The balance between range, precision, and storage capacity must be carefully managed to optimize performance, accuracy, and memory usage. Selecting appropriate data types based on these factors ensures robust and efficient software, particularly in resource-constrained or high-stakes environments.
https://docs.oracle.com/javase/tutorial/java/nutsandbolts/datatypes.html