Rubin's rules ($T=\overline{U}+(1+M^{-1})B$)

For $M>1$ completed-data estimates $Q_m$ with variances $U_m$, pool using $\overline{Q}=M^{-1}{\sum }_m{Q}_m$, $\overline{U}=M^{-1}{\sum }_m{U}_m$, $B=(M-1{)}^{-1}{\sum }_m(Q_m-\overline{Q}{)}^2$, and $T=\overline{U}+(1+M^{-1})B$. The total variance includes within-imputation uncertainty, between-imputation uncertainty and finite-$M$ simulation uncertainty.

Use the pooled estimate and total variance from Rubin's rules. A basic large-complete-sample scalar interval is $\overline{Q}\pm t_{\nu ,1-\alpha /2}\sqrt{T}$, with $\nu =(M-1)[1+\overline{U}/((1+M^{-1})B){]}^2$ for $B>0$. Small complete-data samples need an appropriate degrees-of-freedom adjustment; the normal limit applies as the available degrees of freedom become large.