Bias & Validity Definition

Validity is how well the test or study answers the question it was supposed to answer. With regard to laboratory test results you would use sensitivity and specificity to measure validity. However, the term validity is more commonly used when referring to research. It is basically how valid the conclusions of the study are based on the study’s design and results. There is internal validity which measures how well your results represent what is going on in the sample being studied and external validity which measures how well your results can be applied to other situations (or the overall population).

Bias is a non-random (directional) deviation from the truth. High bias in a study means low validity and vice versa. With regards to research studies bias is problems with the study design or execution that cause you to consistency get distorted results. These results are non-random as you are consistently having the results skewed in the same direction. In most cases this means you are showing a stronger association between the factor being studied and the health outcome. Bias is different than the random error you might see with a low sample size. Bias means there is something fundamentally wrong with the study that is causing you to get incorrect results that are consistently different than the truth. You can’t correct bias by having a larger sample size.

The Ideal Research Study has the following characteristics:
• The study population is similar to the overall population of interest
• The two or more groups in the study should be as close to identical as possible at the start of the study except for the one variable you are trying to isolate
• The different groups should remain close to identical throughout the study. This involves keeping as many patients as possible enrolled in the study until the end and treating the different groups the same except for the variable you are trying to isolate
• All patients are compliant with any treatments or lifestyle changes assigned to them

 

Now that you have finished this video you should check out the next video in the Biostats & Epi sections which covers Confounding, Randomization, Selection Bias & Sampling Bias.

 

 

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