+@node COCHRAN
+@subsection Cochran Q Test
+@vindex Cochran
+@cindex Cochran Q test
+@cindex Q, Cochran Q
+
+@display
+ [ /COCHRAN = varlist ]
+@end display
+
+The Cochran Q test is used to test for differences between three or more groups.
+The data for @var{varlist} in all cases must assume exactly two distinct values (other than missing values).
+
+The value of Q will be displayed and its Asymptotic significance based on a chi-square distribution.
+
+@node FRIEDMAN
+@subsection Friedman Test
+@vindex FRIEDMAN
+@cindex Friedman test
+
+@display
+ [ /FRIEDMAN = varlist ]
+@end display
+
+The Friedman test is used to test for differences between repeated measures when there is no indication that the distributions are normally distributed.
+
+A list of variables which contain the measured data must be given. The procedure prints the sum of ranks for each variable, the test statistic and its significance.
+
+@node KENDALL
+@subsection Kendall's W Test
+@vindex KENDALL
+@cindex Kendall's W test
+@cindex coefficient of concordance
+
+@display
+ [ /KENDALL = varlist ]
+@end display
+
+The Kendall test investigates whether an arbitrary number of related samples come from the
+same population.
+It is identical to the Friedman test except that the additional statistic W, Kendall's Coefficient of Concordance is printed.
+It has the range [0,1] --- a value of zero indicates no agreement between the samples whereas a value of
+unity indicates complete agreement.
+
+
+@node KOLMOGOROV-SMIRNOV
+@subsection Kolmogorov-Smirnov Test
+@vindex KOLMOGOROV-SMIRNOV
+@vindex K-S
+@cindex Kolmogorov-Smirnov test
+
+@display
+ [ /KOLMOGOROV-SMIRNOV (@{NORMAL [@var{mu}, @var{sigma}], UNIFORM [@var{min}, @var{max}], POISSON [@var{lambda}], EXPONENTIAL [@var{scale}] @}) = varlist ]
+@end display
+
+The one sample Kolmogorov-Smirnov subcommand is used to test whether or not a dataset is
+drawn from a particular distribution. Four distributions are supported, @i{viz:}
+Normal, Uniform, Poisson and Exponential.
+
+Ideally you should provide the parameters of the distribution against which you wish to test
+the data. For example, with the normal distribution the mean (@var{mu})and standard deviation (@var{sigma})
+should be given; with the uniform distribution, the minimum (@var{min})and maximum (@var{max}) value should
+be provided.
+However, if the parameters are omitted they will be imputed from the data. Imputing the
+parameters reduces the power of the test so should be avoided if possible.
+
+In the following example, two variables @var{score} and @var{age} are tested to see if
+they follow a normal distribution with a mean of 3.5 and a standard deviation of 2.0.
+@example
+ NPAR TESTS
+ /KOLMOGOROV-SMIRNOV (normal 3.5 2.0) = @var{score} @var{age}.
+@end example
+If the variables need to be tested against different distributions, then a seperate
+subcommand must be used. For example the following syntax tests @var{score} against
+a normal distribution with mean of 3.5 and standard deviation of 2.0 whilst @var{age}
+is tested against a normal distribution of mean 40 and standard deviation 1.5.
+@example
+ NPAR TESTS
+ /KOLMOGOROV-SMIRNOV (normal 3.5 2.0) = @var{score}
+ /KOLMOGOROV-SMIRNOV (normal 40 1.5) = @var{age}.
+@end example
+
+The abbreviated subcommand K-S may be used in place of KOLMOGOROV-SMIRNOV.
+