  This study explores various data-driven methods for performing
background-model selection, and for assigning uncertainty on the
signal-strength estimator that arises due to the choice of background model.
The performance of these methods is evaluated in the context of several
realistic example problems. Furthermore, a novel strategy is proposed that
greatly simplifies the process of performing a bump hunt when little is assumed
to be known about the background. This new approach is shown to greatly reduce
the potential bias in the signal-strength estimator, without degrading the
sensitivity by increasing the variance, and to produce confidence intervals
with valid coverage properties.
