  Assume that a grocery item is sold 1'234 times on a given day. What should an
ideal forecast have predicted for such a well-selling item, on average? More
generally, when considering a given outcome value, should the empirical average
of forecasted expectation values for that outcome ideally match it? Many people
will intuitively answer the first question with "1'234, of course", and affirm
the second. Perhaps surprisingly, such grouping of data by outcome induces a
bias in the evaluation. An evaluation procedure that is aimed at verifying the
absence of bias across velocities, when based on such segregation by outcome,
therefore fools forecast evaluators and incentivizes forecasters to produce
overly exaggerated (extreme) forecasts. Such anticipatory adjustments
jeopardize forecast calibration and clearly worsen the forecast quality - this
problem was named the "Forecaster's Dilemma" by Lerch et al. in 2017
(Statististical Science 32, 106). As a solution to check for bias across
velocities, forecast evaluators should group pairs of forecasts and outcomes by
the predicted values, and evaluate empirical mean outcomes per prediction
bucket. Within a simple mathematical treatment for the number of items sold in
a supermarket, the reader is walked through the dilemma and its circumvention.
