This can skew the dataset and lead to misinterpreted results, if which rows are wrong is not completely random.
Eg if all data from a specific location (or year etc) comes wrong, then this kind of cleaning would just completely exclude this location, which depending on the context may or may not be a problem. Or if values come wrong above a specific threshold. Or any other way that the errors are not in some way randomly distributed.
Removing data is never a neutral choice, and it should always be taken into consideration (which data is removed).
Absolutely. If you have obviously wrong data your choices are generally:
1. Leave the bad data in.
2. Leave the bad data in and flag it as suspect.
3. Omit the dad data.
4. Correct the bad data.
Which is the best choice depends on context and requires judgement. But I find it hard to imagine any situation where option 1 is the right choice.
Obviously the best solution is to do basic validation as the data is entered, so that people can't add a location in the Indian ocean to a UK dataset. It seems rather negligent that they didn't do this.
If you want something to blame, blame the system that allowed the data to be bad in the first place. You're pointing your finger at the wrong people and it's unreasonable of you to call them negligent.
Messy data is a signal. You're wrong to omit signal.
A better solution is to add a field to indicate that "the row looks funny to the person who published the data". Which, I guess is useful to someone?
But deleting data or changing data is effectively corrupting source data, and now I can't trust it.