Prof. Bangerth's comment is completely correct. To add more detail, we can refer to IEEE754 standard which defines the floats as
| sign bit | exponent bits | mantissa |
| 1 bit | 8 bits | 23 bits |
Sign bit represents the sign of the number $+$ or $-$
Exponent bits are signed (using two's complement) and ranges from $-128$ to $127$
Lastly, mantissa has an implicit 1 assumption (without getting into intricacies like subnormal -or denormal- number), so a number in this representation has the form:
$\pm 2^{E} \times 1.\text{mantissa}$. For example;
$3.1415=$0 10000000 10010010000111001010110
or equivalently, $+ 2^{(10000000)_2-128} \times (\color{red}1.10010010000111001010110)_2$ where red coloured $1$ is implicitly assumed to be there.
Now, if you transform the floating point representation back to decimal, you will notice that it is actually equal to $3.14149996185302734375$ which is not equal to $3.1415$. This is because mantissa has only so much space (23 bits in case of floats) and we have to round. This rounding may introduce an error of at most $2^{-23}\approx 10^{-7}$.
Depending on how you define precision, this means that you have either 6 decimal places or 6-7 decimal places of precision.
I wrote this in a rush, I may have made some mistakes. Please be critical of what I am saying here and refer to other sources. And if I said anything wrong, please let me know so I can fix it.