Traditionally, DBMSs are shipped with hundreds of configuration parameters. Since the database performance highly depends on the appropriate settings of the configuration parameters, DBAs spend a lot of their time and effort to find the best parameter values for tuning the performance of the application of interest. In many cases, they rely on their experience and some rules of thumbs. However, time and effort may be wasted by tuning those parameters which may have no or marginal effects. Moreover, tuning effects also vary depending on the expertise of the DBAs, but skilled DBAs are increasingly becoming rare and expensive to employ. To address these problems, we present a Statistical Approach for Ranking Database parameters (SARD), which is based on the Plackett & Burman statistical design methodology. SARD takes the query workload and the number of configuration parameters as inputs, and using only a linear number of experiments, generates a ranking of database parameters based on their relative impacts on the DBMS performance. Preliminary experimental results using TPC-H and PostgreSQL show that SARD generated ranking can correctly identify critical configuration parameters.