The Statevector is a vector of length $2^n$ (where you have $n$ qubits) in which the square of each element's absolute value gives you the probability of getting that bit pattern. So, for example, $[\frac{\sqrt3}4, \frac{\sqrt5}4, \frac{\sqrt6}4, \frac{\sqrt2}4]$ indicates that you have a $\frac{3}{16}$ of getting $|00\rangle$, a $\frac5{16}$ change of getting $|01\rangle$, etc. This is a calculation that can only be done by a simulator keeping track of every possible state, and not by an actual quantum device with $n$ qubits.

Since a statevector simulator knows the exact odds of every possible result (save for floating point errors), it can give a precise answer.

Simulators that return shots rather than statevectors often generate a statevector internally, and that use a method similar to Statevector.sample_memory(shots) to emulate taking quantum measurements on a statevector. The results look less clean and more like coin tossing.

The Statevector is a vector of length $2^n$ (where you have $n$ qubits) in which the square of each element's absolute value gives you the probability of getting that bit pattern. So, for example, $[\frac{\sqrt3}4, \frac{\sqrt5}4, \frac{\sqrt6}4, \frac{\sqrt2}4]$ indicates that you have a $\frac{3}{16}$ chance of getting $|00\rangle$, a $\frac5{16}$ chance of getting $|01\rangle$, etc. This is a calculation that can only be done by a simulator keeping track of every possible state, and not by an actual quantum device with $n$ qubits.

Since a statevector simulator knows the exact odds of every possible result (save for floating point errors), it can give a precise answer.

Simulators that return shots rather than statevectors often generate a statevector internally, and then use a method similar to Statevector.sample_memory(shots) to emulate taking quantum measurements on a statevector. The results look less clean and more like coin tossing.