In addition, to studying design considerations, investigators should also plan to maximize the validity of the study results by minimizing bias and random error.  Bias is a systematic error in the design, recruitment, data collection, or analysis that results in a mistaken estimation of the true effect of the exposure and the outcome, and it usually cannot be “corrected” during analysis.  Random error is the false association between exposure and disease that arises from chance and can arise from two sources - measurement error and sampling variability.  Ways to minimize random error include using large sample sizes, repeating measurements, and selecting random samples to hopefully select a sample that is representative of the population.  

Potential confounding and/or effect modification by variables other than the main exposure/outcome of interest are also very important to consider.  A confounder is a third variable that masks the true relationship between the exposure and outcome, and an effect modifier is a third variable that alters the relationship between the exposure and outcome.  It is important to consider these additional variables before data collection starts to make sure that all important variables are being measured.  Once the data have been collected, variables can be evaluated as potential confounders and/or effect modifiers.