What is the difference between falsifying data and just being wrong? I had to take a standard ethics seminar at my research lab (everyone in my lab must take the seminar).  As one would expect, I learned that the charges associated with falsifying data are serious.  

Whether you falsify data or make a stupid mistake, the end result is that the published work is wrong.  But the charges of falsifying data are much more serious than the charges associated with human error.  

My motivation for making this post is as follows.  Researchers must be allowed some leniency on accuracy, otherwise one would not progress at a reasonable pace.  Nobody is right 100% of the time. Some of the best papers and discoveries have inaccuracies (some are in fact 100% wrong).  However, if your data is innacurate, and the allegation is that you falsified the data, your career takes a major hit.

Question 

What are the specific differences between falsifying results and making a human error?  How does one decipher  between erroneous results (due to human error, or negligence, or incompetence, or an early career mistake, etc) and falsified/fabricated results?  How does one prove results are falsified?  Conversely, how would a researcher prove his/her innocence?
The clear difference is intent.

Human error occurs when people make mistakes accidentally. For instance, in my first paper, I submitted a graph that was physically implausible because of an error in a code I had written, without realizing it at the time. 

However, if I knew the work was wrong or, worse still, if I intentionally made up results, we would cross the boundary from human error to fraud.

Unfortunately, it can be very difficult to detect intentional fraud without some assistance from the perpetrators themselves. A major example of this was the Jan-Hendrik Schön scandal of the early 2000's. Schön's fraud was discovered because he was reusing the same figures to different ends in papers in various journals.