Statistical modelling to make predictions encounters various challenges, including deal- ing with model uncertainty and limited sample size. Model uncertainty arises from the fact that we usually do not fully pre-specify a model before we fit it to a data set. 69, 101 An iterative process is often followed with model checking and model modification. On the other hand, standard statistical methods assume that a model was pre-specified. In that case, parameter estimates such as regression coefficients, their corresponding standard errors, 95% confidence intervals, and p -values are largely unbiased. When the structure of a model was at least partly based on findings in the data, bias may occur, and we underestimate the uncertainty of conclusions drawn from the model.