\begin{table}%t1
\caption{\label{Tab:Prob}Probabilities that a periodogram power ($P_{n}$, $p$, $P$ or $z$) can exceed a given value~($P_{n,0}$, $p_{0}$, $P_{0}$ or $z_{0}$) for different normalizations (from \citealt{Cumming99}).}
\par
%\centering
\small
\begin{tabular}{lll}
\hline
\hline 
Reference level & Range & Probability\\
\hline
population variance & $P_{n}\in[0,\infty)$    & ${\rm Prob}(P_{n}>P_{n,0})=\exp(-P_{n,0})$\\
sample variance     & $p\in[0,1]$             & ${\rm Prob}(p>p_{0})=\left(1-p_{0}\right)^{\frac{N-3}{2}}$\\
-- '' --            & $P\in[0,\frac{N-1}{2}]$ & ${\rm Prob}(P>P_{0})=\left(1-\frac{2P}{N-1}\right)^{\frac{N-3}{2}}$\\
residual variance   & $z\in[0,\infty)$        & ${\rm Prob}(z>z_{0})=\left(1+\frac{2z_{0}}{N-3}\right)^{-\frac{N-3}{2}}$\\
\hline
\end{tabular}
\par
\end{table}