Greater Precision
TLDR: Greater precision refers to the ability of a data type or system to represent numerical values with a higher number of significant digits. This characteristic is essential in computations where small errors can propagate or where exact representation is required, such as in scientific simulations, cryptography, and financial modeling. Greater precision often requires increased storage capacity and computational resources, balancing accuracy and performance.
https://en.wikipedia.org/wiki/Numerical_analysis
Greater precision is typically achieved by using data types with larger bit allocations for the significand in floating-point formats or by employing arbitrary precision types. For example, a double-precision float in IEEE 754 (64 bits) provides approximately 15–17 significant decimal digits, whereas a single-precision float (32 bits) provides only about 7. For tasks requiring even more precision, tools like BigDecimal in Java or mpfr in C++ allow for arbitrary precision, though at a cost to performance and memory usage.
https://standards.ieee.org/standard/754-2019.html
The need for greater precision often arises in fields like scientific computing, where calculations involve very large or very small values, and slight inaccuracies can significantly impact results. Developers must carefully select data types or libraries based on the precision requirements of their applications. While greater precision enhances accuracy, it also increases computational complexity, making it vital to strike a balance for efficient and effective program execution.
https://docs.oracle.com/javase/tutorial/java/nutsandbolts/datatypes.html