  Getting good speedup -- let alone high parallel efficiency -- for
parallel-in-time (PinT) integration examples can be frustratingly difficult.
The high complexity and large number of parameters in PinT methods can easily
(and unintentionally) lead to numerical experiments that overestimate the
algorithm's performance. In the tradition of Bailey's article "Twelve ways to
fool the masses when giving performance results on parallel computers", we
discuss and demonstrate pitfalls to avoid when evaluating performance of PinT
methods. Despite being written in a light-hearted tone, this paper is intended
to raise awareness that there are many ways to unintentionally fool yourself
and others and that by avoiding these fallacies more meaningful PinT
performance results can be obtained.
