  A scaled Chebyshev node distribution is studied in this paper. It is proved
that the node distribution is optimal for interpolation in $C_M^{s+1}[-1,1]$,
the set of $(s+1)$-time differentiable functions whose $(s+1)$-th derivatives
are bounded by a constant $M>0$. Node distributions for computing spectral
differentiation matrices are proposed and studied. Numerical experiments show
that the proposed node distributions yield results with higher accuracy than
the most commonly used Chebyshev-Gauss-Lobatto node distribution.
