You need to define what "error" means; typically it is an estimate of the standard deviation based on a series of measurements. If you take a series of measurements, you can estimate the standard deviation of the population. You can also estimate the mean and the standard deviation of the mean. When you report your result you should report $\mu \pm \sigma_{\mu}$ where $\mu$ is the estimate of the mean from your measurements and $\sigma_{\mu}$ is your estimate of the standard deviation of the mean, not the standard deviation of the population which you can also estimate.

You can also establish a confidence interval based on the measurements, and some call that the accuracy.

See my answer to Uncertainty in repetitive measurements this exchange.

You need to define what "error" means; typically it is an estimate of the standard deviation based on a series of measurements. If you take a series of measurements, you can estimate the standard deviation of the population. You can also estimate the mean and the standard deviation of the mean. When you report your result you should report $\mu \pm \sigma_{\mu}$ where $\mu$ is the estimate of the mean from your measurements and $\sigma_{\mu}$ is your estimate of the standard deviation of the mean, not the standard deviation of the population which you can also estimate. See my answer to Uncertainty in repetitive measurements this exchange for details. If you told me "my result is x with 10% error", without more information I would assume that based on your measurements, x is the mean and 0.1x is the standard deviation of the mean.

You can also establish a confidence interval based on the measurements, and some call that the accuracy. See discussions of confidence interval online or in a statistics text such as Probability and Statistics for the Engineering, Computing, and Physical Sciences by Dougherty.