+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 separate
+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.
+
+@node KRUSKAL-WALLIS
+@subsection Kruskal-Wallis Test
+@vindex KRUSKAL-WALLIS
+@vindex K-W
+@cindex Kruskal-Wallis test
+
+@display
+ [ /KRUSKAL-WALLIS = varlist BY var (lower, upper) ]
+@end display
+
+The Kruskal-Wallis test is used to compare data from an
+arbitrary number of populations. It does not assume normality.
+The data to be compared are specified by @var{varlist}.
+The categorical variable determining the groups to which the
+data belongs is given by @var{var}. The limits @var{lower} and
+@var{upper} specify the valid range of @var{var}. Any cases for
+which @var{var} falls outside [@var{lower}, @var{upper}] will be
+ignored.
+
+The mean rank of each group as well as the chi-squared value and significance
+of the test will be printed.
+The abbreviated subcommand K-W may be used in place of KRUSKAL-WALLIS.
+
+
+@node MANN-WHITNEY
+@subsection Mann-Whitney U Test
+@vindex MANN-WHITNEY
+@vindex M-W
+@cindex Mann-Whitney U test
+@cindex U, Mann-Whitney U
+
+@display
+ [ /MANN-WHITNEY = varlist BY var (group1, group2) ]
+@end display
+
+The Mann-Whitney subcommand is used to test whether two groups of data come from different populations.
+The variables to be tested should be specified in @var{varlist} and the grouping variable, that determines to which group the test variables belong, in @var{var}.
+@var{Var} may be either a string or an alpha variable.
+@var{Group1} and @var{group2} specify the
+two values of @var{var} which determine the groups of the test data.
+Cases for which the @var{var} value is neither @var{group1} or @var{group2} will be ignored.
+
+The value of the Mann-Whitney U statistic, the Wilcoxon W, and the significance will be printed.
+The abbreviated subcommand M-W may be used in place of MANN-WHITNEY.
+
+@node MCNEMAR
+@subsection McNemar Test
+@vindex MCNEMAR
+@cindex McNemar test
+
+@display
+ [ /MCNEMAR varlist [ WITH varlist [ (PAIRED) ]]]
+@end display
+
+Use McNemar's test to analyse the significance of the difference between
+pairs of correlated proportions.