I've performed $m$ fits of datasets $Y=\theta X+b$ coming from different experiments. As a result, I have $m$ estimates $\theta_1,\theta_2,\dots \theta_m$ where $\theta_{k}=(\theta_{k,1},\dots\theta_{k,n})$ is a vector of slopes. For each slope $\theta_{k,i}$, I have a confidence interval $[\underline{\theta}_{k,i},\bar{\theta}_{k,i}]$.
I want to test a hypothesis, which these slopes (i.e. $\theta_{\cdot,j}$ with data $\theta_{1,j},\dots\theta_{m,j}$) come from Gamma distribution. The easiest way is to use Kolmogorov-Smirnov or Chi-square tests for mean values. Nevertheless, I don't want to lose information, which contains in confidence intervals $[\underline{\theta}_{k,i},\bar{\theta}_{k,i}]$.
Do you have any idea how to deal with such a problem?