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It's a tricky area: Decimal numbers can be represented exactly. In contrast, numbers like 1.1 do not have an exact representation in binary floating point. End users typically would not expect 1.1 to display as 1.1000000000000001 as it does with binary floating point. The exactness carries over into arithmetic. In decimal floating point, 0.1 + 0.1 + 0.1 - 0.3 is exactly equal to zero. In binary floating point, the result is 5.5511151231257827e-017. While near to zero, the differences prevent reliable equality testing and differences can accumulate. For this reason, decimal is preferred in accounting applications which have strict equality invariants. So you have to be carefull how you store floating point decimals in binary.

It can also be used in a fraction. It must be simplufied then reduced and multiplied.

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Q: Floating point representation in binary why is it important to represent and how the decimal is placed in the binary?
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