The single-precision floating-point or double-precision floating-point has finite precision so loss of significance can happen and cause surprises.

Let’s take float, which has 23 bits mantissa, as an example. 198705381 as an integer has the binary representation 00001011 11011000 00000000 11100101. 198705381.0f as a float has the binary representation 0 10011010 01111011000000000001110. Here a round-down happens due to the default IEEE 754 rounding mode round to the nearest, ties to even and 198705381.0f is rounded down to 198705376.0f.

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std::cout << std::setprecision(16) << 198705381.0f << "\n"; // output is 198705376

199698905 as an integer has the binary representation 00001011 11100111 00101001 11011001. 199698905.0f as a float has the binary representation 0 10011010 01111100111001010011110. Here a round-up happens and 199698905.0f is rounded up to 199698912.0f.

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std::cout << std::setprecision(16) << 199698905.0f << "\n"; // output is 199698912

Due to the loss of significance, many surprises can happen:

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std::cout << 198705381.0f - 198705380.0f << "\n";  // output is 0 since they are both rounded down to the same value

std::cout << 198705381.0f * 1.005f - 199698905.0f << "\n"; // output is negative (should be positive if we use double instead of float) since one is rounded down and the other is rounded up

References

[1] https://benjaminjurke.com/content/articles/2015/loss-of-significance-in-floating-point-computations
[2] https://www.h-schmidt.net/FloatConverter/IEEE754.html