Removing outliers. Would it be called cherrypicking data?
One major pitfall is going into analyzing the data without an a priori protocol to deal with outliers. Time to time, tension rises between analysts and investigators on whether a point should be removed or not. Investigator may wish to keep it because it drives the significant result; analyst may be anxious to keep it because, well, it drives the significant result. Much can be avoided if there is a agreed-upon standard.

Anyhow, here are some suggestions (slightly scrabbled):


Reconcile if it's an instrument or data entry error.
If it's a plausible value but behaving like an outlier, a few methods exist:


Do not bury plausible extreme values in favor of showing the after-trimming data only.
Consider reporting with and without it, discuss the differences.
Use statistics that are more tolerant to extreme values. For instance: Non-parametric statistics or robust estimation methods.

Keep all written record about related changes, make sure date, time, person, decision made, etc. are all recorded.
If correction needs to be made, do not change the raw data. Do so using script/syntax and save that syntax for reference.
Explain all outlier-related data change in the report/manuscript. Reasons and methods should be provided so that your analysis can be replicated.
If there isn't a protocol, set one up now before more instances like this came up.
Do not identify outlier after data analysis results are known. If possible, discuss with your colleagues about the outlier without telling them the hypothesis test results.