Adjusting for Confounding Variables I have data from human participants in a study. There are more females in the study (60%) and males are older. I have a binary categorical variable $O$. If those who are $True$ for $O$ are older, do I need to correct for sex and/or age? Maybe those $True$ for $O$ contain more men. What concepts or methods should I use to determine this? I'm using R. 
 A: Edited several times to reflect comments
I realized I should give an example of what I meant by "what your model looks like now." From what you've said, I'm assuming that $Variant$ or $O$ is your dependent or outcome variable and that you're starting with something like the following:
$Variant = \beta_0 + \beta_1(Female) + \beta_2(Age) + \beta_3(Treatment) + \varepsilon$
Where I've inserted "Treatment" you can also think of any other sort of indicator that may be relevant to you (and in fact, if you're really dealing with experimental data, I would recommend considering something other than regression!)
If that's the case (and assuming you're confident in your sampling methods, data collection procedures, decision to use regression, etc.), then the answers to your questions are in the results of your regression model -- post the results and maybe we can help you interpret them.
If $Variant$ is on the other side of the equation (i.e. if it's a covariate or an independent variable), then you might want to think about interaction terms. They can help you test for associations in your data that are not simply additive. 
From your response to my comment above (and below), it sounds like the model might look more like this (I've now replaced all the occurrences of $O$ with $Variant$ just to distinguish $O$ from $\theta$):
$Y = \beta_0 + \beta_1(Female) + \beta_2(Age) + \beta_3(Variant) + \varepsilon$
In that case, I would definitely consider introducing interaction terms between $Age$ and $Variant$ as well as between $Gender$ and $Variant$.
