Store Numbers Resolution For Free

A float has 23 bits of mantissa, and 2^23 is 8,388,608. 23 bits let you store all 6 digit numbers or lower, and most of the 7 digit numbers. This means that floating point numbers have between 6 and 7 digits of precision, regardless of exponent.

The smallest change that can be represented in floating point representation is called as precision. The fractional part of a single precision normalized number has exactly 23 bits of resolution, (24 bits with the implied bit).

precision float only has about 7 decimal digits of precision (actually the log base 10 of 223, or about 6.92 digits of precision). The greater the integer part is, the less space is left for floating part precision.

They both use 32 bit IEEE-754 floating point numbers (single precision). This format has a 24 bit mantissa (if you count the hidden bit), so the effective resolution is between one part in 223 (eight million) and one part in 224 (16 million). This supports six or seven decimal digits of resolution.

A single precision floating-point number is a short (32 bits) floating-point number. The range of single precision floating-point numbers is about -7.2E+75 to 7.2E+75. In this range, the largest negative value is about -5.4E-79, and the smallest positive value is about 5.4E-079.

They both use 32 bit IEEE-754 floating point numbers (single precision). This format has a 24 bit mantissa (if you count the hidden bit), so the effective resolution is between one part in 223 (eight million) and one part in 224 (16 million). This supports six or seven decimal digits of resolution.

Single-precision floating-point format (sometimes called FP32 or float32) is a computer number format, usually occupying 32 bits in computer memory; it represents a wide dynamic range of numeric values by using a floating radix point.