That only helps you spot outright fabrication though, doesn't it? I would assume that this only happens in the minority of cases.
The much bigger issue is people trying many different statistical tests until they find the one that "works", removing outliers to make the data look better or just neglecting to mention things like p-values or confidence intervals in the hope that nobody would notice.
Much harder to spot, but potentially as misleading as just making numbers up...
most real datasets apparently have outliers... you should not remove them if you want it to look real (I heard this from this course: https://class.coursera.org/dataanalysis-002/)
The much bigger issue is people trying many different statistical tests until they find the one that "works", removing outliers to make the data look better or just neglecting to mention things like p-values or confidence intervals in the hope that nobody would notice.
Much harder to spot, but potentially as misleading as just making numbers up...