|         0.0001
    98 | {98, 98} | {f,f} |      0.01 |         0.0001
    99 | {99, 99} | {f,f} |      0.01 |         0.0001
(100 rows)
</programlisting>

    This confirms there are 100 distinct combinations in the two columns, and
    all of them are about equally likely (1% frequency for each one).  The
    base frequency is the frequency computed from per-column statistics, as if
    there were no multi-column statistics. Had there been any null values in
    either of the columns, this would be identified in the
    <structfield>nulls</structfield> column.
   </para>

   <para>
    When estimating the selectivity, the planner applies all the conditions
    on items in the <acronym>MCV</acronym> list, and then sums the frequencies
    of the matching ones. See <function>mcv_clauselist_selectivity</function>
    in <filename>src/backend/statistics/mcv.c</filename> for details.
   </para>

   <para>
    Compared to functional dependencies, <acronym>MCV</acronym> lists have two
    major advantages. Firstly, the list stores actual values, making it possible
    to decide which combinations are compatible.

<programlisting>
EXPLAIN (ANALYZE, TIMING OFF, BUFFERS OFF) SELECT * FROM t WHERE a = 1 AND b = 10;
                                 QUERY PLAN
-------------------------------------------------------------------&zwsp;--------
 Seq Scan on t  (cost=0.00..195.00 rows=1 width=8) (actual rows=0.00 loops=1)
   Filter: ((a = 1) AND (b = 10))
   Rows Removed by Filter: 10000
</programlisting>

    Secondly, <acronym>MCV</acronym> lists handle a wider range of clause types,
    not just equality clauses like functional dependencies. For example,
    consider the following range query for the same table:

<programlisting>
EXPLAIN (ANALYZE, TIMING OFF, BUFFERS OFF) SELECT * FROM t WHERE a &lt;= 49 AND b &gt; 49;
                                QUERY PLAN
-------------------------------------------------------------------&zwsp;--------
 Seq Scan on t  (cost=0.00..195.00 rows=1 width=8) (actual rows=0.00 loops=1)
   Filter: ((a &lt;= 49) AND (b &gt; 49))
   Rows Removed by Filter: 10000
</programlisting>

   </para>

  </sect2>

 </sect1>

 <sect1 id="planner-stats-security">
  <title>Planner Statistics and Security</title>

  <para>
   Access to the table <structname>pg_statistic</structname> is restricted to
   superusers, so that ordinary users cannot learn about the contents of the
   tables of other users from it.  Some selectivity estimation functions will
   use a user-provided operator (either the operator appearing in the query or
   a related operator) to analyze the stored statistics.  For example, in order
   to determine whether a stored most common value is applicable, the
   selectivity estimator will have to run the appropriate