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I have had the following situation happen several times during my research career:  I write code to analyze data; there is some expectation about what the results will be; after running the program, the results are not what was expected; I go back and carefully check the code to make sure there are no errors; sometimes I find an error No matter how careful you are when it comes to writing computer code, I think you are more likely to find a mistake if you think there is one.  Unexpected results lead one to suspect a coding error more than expected results do. In general, researchers usually do have general expectations about what they will find (e.g., the drug will not increase risk of the disease; the toxin will not decrease risk of cancer). Consider the following graphic: Here, the green region is consistent with what our expectations are.  For example, if we expect a relative risk (RR) of about 1.5, we might not be too surprised if the estimated RR is between (e.g.) 0.9 and 2.0.  Anything above 2.0 or below 0.9 might make us highly suspicious of an error -- that's the red region.  Estimates in the red region are likely to trigger serious coding error investigation.  Obviously, if there is no coding error then the paper will get submitted with the surprising results. Error scenarios Let's assume that there is a coding error that causes the estimated effect to differ from the true effect (assume sample size large enough to ignore sampling variability). Consider the following scenario: Type A. Here, the estimated value is biased, but it's within the expected range.  In this scenario, error checking is probably more casual and less likely to be successful. Next, consider this scenario: Type B. In this case, the estimated value is in the red zone.  This triggers aggressive error checking of the type that has a higher success rate. Finally: Type C. In this case it's the true value that differs from our expectations.  However, the estimated value is