When the manipulator's tool position $p_\text{tool} = (x_\text{tool}, y_\text{tool}, z_\text{tool})$ is different from the point $p_\text{target} = (x_\text{target}, y_\text{target}, z_\text{target})$ where the manipulator should go to, we say there is position error.
Intuitively, position error $e$ should tell us how far the current tool point $p_\text{tool}$ is from the expected point $p_\text{target}$. So a natural choice of the formula to use to calculate position error given $p_\text{tool}$ and $p_\text{target}$ is how we would measure the distance between two points in a 3D space, i.e.
$$
e = \Vert p_\text{tool} - p_\text{target} \Vert_2 = \sqrt{(x_\text{tool} - x_\text{target})^2 + (y_\text{tool} - y_\text{target})^2 + (z_\text{tool} - z_\text{target})^2}.
$$
However, in general, the function used to calculate position errors, does not have to be the square root of squared differences as above but can be any distance metric.
The root-mean-square error is kind of similar to the position error above (in case $L_2$-norm is used as the distance metric), but used in a different context.
While the position error tells us how much a single measurement ($p_\text{tool}$) deviates from the expected value ($p_\text{target}$), the root-mean-square shows how much a given set of measurements (for example, $\{p_\text{tool}^{1}, p_\text{tool}^{2}, \ldots, p_\text{tool}^{N}\}$) deviates from expected values ($\{p_\text{target}^{1}, p_\text{target}^{2}, \ldots, p_\text{target}^{N}\}$). It might be used when, say, you control the manipulator to go along a path of $10$ points and you want to see how well the manipulator tracks those $10$ points, for example.
While generally a position error is a single number as mentioned above, at times we might want to express a position error as a vector (as @Akshay mentioned in this answer) as it gives us which direction the measured point deviates from the expected one, which could be useful for further use.