Without knowing what variables are missing in statistical data, how can you trust the results of any statistical evidence. How could one analysis be more accurate than another.

This is an example of the Fallacy of Gray. No statistical analysis can account for absolutely everything, but one analysis can be more accurate than another, by accounting for more of the important things. A statistician earns trust the same way anyone else does: by being right in cases you can verify, and by presenting evidence of good and detailed reasoning.