When online user testing, and dealing with large sample sizes , the 
higher the number of participants, the higher the likelihood of inaccurate data collection. The main culprits for bad data are those participants who are just doing it for the money and not taking the test seriously.
How can we identify these types of cheaters, and what kind of quality control methods can be used to make sure the data is accurate?
Jeff Sauro at Measuring Usability has written a great piece on how to catch cheaters , with some fascinating insights and statistics about cheaters. It also describes which measures can be taken minimize the damage caused by cheaters . The full article can be found here. We highly recommend it.
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