  We discuss the practice of directly or indirectly assuming a model for the
number of points when modelling spatial point patterns even though it is rarely
possible to validate such a model in practice since most point pattern data
consist of only one pattern. We therefore explore the possibility to condition
on the number of points instead when fitting and validating spatial point
process models. In a simulation study with different popular spatial point
process models, we consider model validation using global envelope tests based
on functional summary statistics. We find that conditioning on the number of
points will for some functional summary statistics lead to more narrow
envelopes and that it can also be useful for correcting for some
conservativeness in the tests when testing composite hypothesis. However, for
other functional summary statistics, it makes little or no difference to
condition on the number of points. When estimating parameters in popular
spatial point process models, we conclude that for mathematical and
computational reasons it is convenient to assume a distribution for the number
of points.
