There is no universal solution to your problem and there really cannot be without specific knowledge of the population. It the value is ridiculous for the population, leave it out. But you should have done this before you tried to analyze with it (easy for me to say).

You can consider Grubb's Test and its variants.

MAD always sounds good but whenever I've used it, it performs poorly, at best.

In a particular analysis, leverage analysis and various leave-out algorithms evaluate the impact of the point...did it really impact the analysis.

Nonparametric (distribution-free) analysis is a better idea.

In many cases, a log-transform will make outliers go poof.

In many cases, removing one outlier will expose another...and so on...until it is difficult to argue that you are not pruning your data like a topiary -- "What a gorgeous elephant!".

There is no universal solution to your problem and there really cannot be without specific knowledge of the population. It the value is ridiculous for the population, leave it out. But you should have done this before you tried to analyze with it (easy for me to say).

You can consider Grubbs' Test and its variants.

MAD always sounds good but whenever I've used it, it performs poorly, at best.

In a particular analysis, leverage analysis and various leave-out algorithms evaluate the impact of the point...did it really impact the analysis.

Nonparametric (distribution-free) analysis is a better idea.

In many cases, a log-transform will make outliers go poof.

In many cases, removing one outlier will expose another...and so on...until it is difficult to argue that you are not pruning your data like a topiary -- "What a gorgeous elephant!".