# Why do we stabilize variance?

I came across variance stabilizing transformation while reading Kaggle Essay Eval method. They use a variance stabilization transformation to transform kappa values before taking their mean and then transform them back. Even after reading the wiki on variance stabilizing transforms I can't understand, why do we actually stabilize variances? What benefit do we gain by this?

• Usually the intent is to make the (asymptotic) variance independent of the parameter of interest. This is particularly important in inference where we need to know the reference distribution to calculate related quantities of interest. Commented Dec 18, 2012 at 16:19

• But here the transformation is used to to compute a mean by $f^{-1}\left( {1\over n} \sum_i f(\kappa_i) \right)$. I really don’t see the point. For me, this would be the way to go for confidence interval estimation, but for point estimation it just introduces a bias. Commented Dec 19, 2012 at 15:31
• Syntax is [link](http://example.com) Commented Dec 19, 2012 at 15:40