How to compensate for small errors that could greatly distort observed ratios? Lets say for two samples, treatment and control, there are three constituent molecules each and their corresponding amounts are as follows:
Tr. Ct.  
2   1
5   2
6   3

I wish to find out the individual ratios for these three constituent molecules. But however if for some reason the amount of treatment sample was more due to experimental error  it might increase the ratio Tr:Ct artificially and vice versa. Is there any way to compensate for this effect for ratio calculation  by normalizing or any other techniques?
 A: I think I understand your question.  When you take ratios of random variables the ratios can be hihgly variable.  In fact as discussed in other recent questions if the denominator can be 0 or very close to it the ratio may not even have a finite mean let alone a variance.  The Cauchy distribution is the canonical example.  
Let R=U/V be the random variable you are interested in.  As long as U and V have variances and V has a distribution bounded away from 0 R will have a finite variance that can be approximated by the delta method See this recent post.
Using the formula given by the delta method and plugging in sample estimates to get an estimate of this variance you have a good idea as to how variable your estimate is.  If you can derive the density of R you should be able to construct confidence intervals for these ratios that account for this high variability.
You can't directly compensate for the small errors that propagate large error in the ratio because you don't know what they are or when they occur.  But the variance estimate provides a measure of how these error affect the ratio.
