The Friedman test investigates the score differences between
matched samples of size
, the scores in the
th sample being denoted by
(Thus the sample scores may be regarded as a two-way table with
rows and
columns.) The hypothesis under test,
, often called the null hypothesis, is that the samples come from the same population, and this is to be tested against the alternative hypothesis
that they come from different populations.
The test is based on the observed distribution of score rankings between the matched observations in different samples.
The test proceeds as follows
(a) |
The scores in each column are ranked, denoting the rank within column of the observation in row . Average ranks are assigned to tied scores. |
(b) |
The ranks are summed over each row to give rank sums , for . |
(c) |
The Friedman test statistic is computed, where
|
G08AEF returns the value of
, and also an approximation,
, to the significance of this value. (
approximately follows a
distribution, so large values of
imply rejection of
).
is rejected by a test of chosen size
if
. The approximation
is acceptable unless
and
, or
and
, or
and
; for
, tables should be consulted (e.g.,
Siegel (1956)); for
the Sign test (see
G08AAF) or Wilcoxon test (see
G08AGF) is in any case more appropriate.
If on entry
or
, explanatory error messages are output on the current error message unit (as defined by
X04AAF).
For estimates of the accuracy of the significance
, see
G01ECF. The
approximation is acceptable unless
and
, or
and
, or
and
.
This example is taken from page 169 of
Siegel (1956). The data relates to training scores of three matched samples of
rats, trained under three different patterns of reinforcement.