1.
Good statistics research is **application relevant**. There must be
real data sets for which the proposed statistical procedure(s) are useful. The
use of such procedure(s) and the results must be validated by experts from the
disciplines, and by leading statisticians, rather than by a closely-knitted
tiny faction of like-minded people. To see examples of such research, one only
needs to read the books by some of today’s leading statisticians Bosq
(1998), Doukhan (1993), Fan and Yao (2003), Härdle (1989), Hastie and
Tibshirani (1990), Horowitz (1998), Li and Racine (2007), Ruppert, Wand and
Carroll (2003).

2.
Good statistics research is **mathematically sophisticated**. It almost
always involves using deep results from analytical theory of special functions
(Besov space, Sobolev space, kernel, spline and wavelets/orthogonal series);
empirical processes; extreme value theory; geometry (statistics on Riemannian
manifold); large random matrices (eigenvalues and norms, inversion, etc.);
dependent data; normal approximations; stochastic calculus; uniform
convergence, etc. Simple algebra plus some calculus may be sufficient in the
early development stage of a new area until problems that can be solved by such
are exhausted.

3.
Good statistics research is **computationally challenging**.
Implementation of good statistical method may require heavy programming in
either matrix/array based Matlab/R/SASIML/S-plus or C++/Fortran. High power
computing for large data necessitates such efforts. Simply downloading free
software written by experts and running on one's own data does not qualify as
original substantive research.

4.
Good statistics research leads
to **theoretically superior** methods,
often enjoying such neat theoretical properties as oracle efficiency, exact and
simple form of asymptotic distribution, uniform confidence regions, etc. In
some cases, weak consistency may be the best one can achieve. To develop
theoretically superior methods does not mean to be “theoretical” in
the negative sense. Some very theoretical people have had significant impacts
on the development of contemporary statistical methodology because they are
willing to work on the kind of theoretical problems of broad applications, rather
than those problems designed by themselves only for their own entertainment.

5.
Good statistics research
produces **user-friendly** procedures,
which are intuitively appealing, fast, numerically accurate and easily
interpretable. Again, it demands hard work on the statisticians to design such
methods and study their properties, leaving the users all convenience.