  Estimating errors is a crucial part of any scientific analysis. Whenever a
parameter is estimated (model-based or not), an error estimate is necessary.
Any parameter estimate that is given without an error estimate is meaningless.
Nevertheless, many (undergraduate or graduate) students have to teach such
methods for error estimation to themselves when working scientifically for the
first time. This manuscript presents an easy-to-understand overview of
different methods for error estimation that are applicable to both model-based
and model-independent parameter estimates. These methods are not discussed in
detail, but their basics are briefly outlined and their assumptions carefully
noted. In particular, the methods for error estimation discussed are grid
search, varying $\chi^2$, the Fisher matrix, Monte-Carlo methods, error
propagation, data resampling, and bootstrapping. Finally, a method is outlined
how to propagate measurement errors through complex data-reduction pipelines.
