G08 Chapter Contents
G08 Chapter Introduction
NAG Library Manual

# NAG Library Routine DocumentG08AJF

Note:  before using this routine, please read the Users' Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details.

## 1  Purpose

G08AJF calculates the exact tail probability for the Mann–Whitney rank sum test statistic for the case where there are no ties in the two samples pooled together.

## 2  Specification

 SUBROUTINE G08AJF ( N1, N2, TAIL, U, P, WRK, LWRK, IFAIL)
 INTEGER N1, N2, LWRK, IFAIL REAL (KIND=nag_wp) U, P, WRK(LWRK) CHARACTER(1) TAIL

## 3  Description

G08AJF computes the exact tail probability for the Mann–Whitney $U$ test statistic (calculated by G08AHF and returned through the parameter U) using a method based on an algorithm developed by Harding (1983), and presented by Neumann (1988), for the case where there are no ties in the pooled sample.
The Mann–Whitney $U$ test investigates the difference between two populations defined by the distribution functions $F\left(x\right)$ and $G\left(y\right)$ respectively. The data consist of two independent samples of size ${n}_{1}$ and ${n}_{2}$, denoted by ${x}_{1},{x}_{2},\dots ,{x}_{{n}_{1}}$ and ${y}_{1},{y}_{2},\dots ,{y}_{{n}_{2}}$, taken from the two populations.
The hypothesis under test, ${H}_{0}$, often called the null hypothesis, is that the two distributions are the same, that is $F\left(x\right)=G\left(x\right)$, and this is to be tested against an alternative hypothesis ${H}_{1}$ which is
• ${H}_{1}$: $F\left(x\right)\ne G\left(y\right)$; or
• ${H}_{1}$: $F\left(x\right), i.e., the $x$'s tend to be greater than the $y$'s; or
• ${H}_{1}$: $F\left(x\right)>G\left(y\right)$, i.e., the $x$'s tend to be less than the $y$'s,
using a two tailed, upper tailed or lower tailed probability respectively. You select the alternative hypothesis by choosing the appropriate tail probability to be computed (see the description of parameter TAIL in Section 5).
Note that when using this test to test for differences in the distributions one is primarily detecting differences in the location of the two distributions. That is to say, if we reject the null hypothesis ${H}_{0}$ in favour of the alternative hypothesis ${H}_{1}$: $F\left(x\right)>G\left(y\right)$ we have evidence to suggest that the location, of the distribution defined by $F\left(x\right)$, is less than the location, of the distribution defined by $G\left(y\right)$.
G08AJF returns the exact tail probability, $p$, corresponding to $U$, depending on the choice of alternative hypothesis, ${H}_{1}$.
The value of $p$ can be used to perform a significance test on the null hypothesis ${H}_{0}$ against the alternative hypothesis ${H}_{1}$. Let $\alpha$ be the size of the significance test (that is, $\alpha$ is the probability of rejecting ${H}_{0}$ when ${H}_{0}$ is true). If $p<\alpha$ then the null hypothesis is rejected. Typically $\alpha$ might be $0.05$ or $0.01$.

## 4  References

Conover W J (1980) Practical Nonparametric Statistics Wiley
Harding E F (1983) An efficient minimal-storage procedure for calculating the Mann–Whitney U, generalised U and similar distributions Appl. Statist. 33 1–6
Neumann N (1988) Some procedures for calculating the distributions of elementary nonparametric teststatistics Statistical Software Newsletter 14(3) 120–126
Siegel S (1956) Non-parametric Statistics for the Behavioral Sciences McGraw–Hill

## 5  Parameters

1:     N1 – INTEGERInput
On entry: the number of non-tied pairs, ${\mathit{n}}_{1}$.
Constraint: ${\mathbf{N1}}\ge 1$.
2:     N2 – INTEGERInput
On entry: the size of the second sample, ${\mathit{n}}_{2}$.
Constraint: ${\mathbf{N2}}\ge 1$.
3:     TAIL – CHARACTER(1)Input
On entry: indicates the choice of tail probability, and hence the alternative hypothesis.
${\mathbf{TAIL}}=\text{'T'}$
A two tailed probability is calculated and the alternative hypothesis is ${H}_{1}:F\left(x\right)\ne G\left(y\right)$.
${\mathbf{TAIL}}=\text{'U'}$
An upper tailed probability is calculated and the alternative hypothesis ${H}_{1}:F\left(x\right), i.e., the $x$'s tend to be greater than the $y$'s.
${\mathbf{TAIL}}=\text{'L'}$
A lower tailed probability is calculated and the alternative hypothesis ${H}_{1}:F\left(x\right)>G\left(y\right)$, i.e., the $x$'s tend to be less than the $y$'s.
Constraint: ${\mathbf{TAIL}}=\text{'T'}$, $\text{'U'}$ or $\text{'L'}$.
4:     U – REAL (KIND=nag_wp)Input
On entry: $U$, the value of the Mann–Whitney rank sum test statistic. This is the statistic returned through the parameter U by G08AHF.
Constraint: ${\mathbf{U}}\ge 0.0$.
5:     P – REAL (KIND=nag_wp)Output
On exit: the exact tail probability, $p$, as specified by the parameter TAIL.
6:     WRK(LWRK) – REAL (KIND=nag_wp) arrayWorkspace
7:     LWRK – INTEGERInput
On entry: the dimension of the array WRK as declared in the (sub)program from which G08AJF is called.
Constraint: ${\mathbf{LWRK}}\ge \left({\mathbf{N1}}×{\mathbf{N2}}\right)/2+1$.
8:     IFAIL – INTEGERInput/Output
On entry: IFAIL must be set to $0$, $-1\text{​ or ​}1$. If you are unfamiliar with this parameter you should refer to Section 3.3 in the Essential Introduction for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value $-1\text{​ or ​}1$ is recommended. If the output of error messages is undesirable, then the value $1$ is recommended. Otherwise, if you are not familiar with this parameter, the recommended value is $0$. When the value $-\mathbf{1}\text{​ or ​}\mathbf{1}$ is used it is essential to test the value of IFAIL on exit.
On exit: ${\mathbf{IFAIL}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see Section 6).

## 6  Error Indicators and Warnings

If on entry ${\mathbf{IFAIL}}={\mathbf{0}}$ or $-{\mathbf{1}}$, explanatory error messages are output on the current error message unit (as defined by X04AAF).
Errors or warnings detected by the routine:
${\mathbf{IFAIL}}=1$
 On entry, ${\mathbf{N1}}<1$, or ${\mathbf{N2}}<1$.
${\mathbf{IFAIL}}=2$
 On entry, ${\mathbf{TAIL}}\ne \text{'T'}$, $\text{'U'}$ or $\text{'L'}$.
${\mathbf{IFAIL}}=3$
 On entry, ${\mathbf{U}}<0.0$.
${\mathbf{IFAIL}}=4$
 On entry, ${\mathbf{LWRK}}<\left({\mathbf{N1}}×{\mathbf{N2}}\right)/2+1$.

## 7  Accuracy

The exact tail probability, $p$, is computed to an accuracy of at least $4$ significant figures.

The time taken by G08AJF increases with ${n}_{1}$ and ${n}_{2}$ and the product ${n}_{1}{n}_{2}$.

## 9  Example

This example finds the Mann–Whitney test statistic, using G08AHF for two independent samples of size $16$ and $23$ respectively. This is used to test the null hypothesis that the distributions of the two populations from which the samples were taken are the same against the alternative hypothesis that the distributions are different. The test statistic, the approximate normal statistic and the approximate two tail probability are printed. G08AJF is then called to obtain the exact two tailed probability. The exact probability is also printed.

### 9.1  Program Text

Program Text (g08ajfe.f90)

### 9.2  Program Data

Program Data (g08ajfe.d)

### 9.3  Program Results

Program Results (g08ajfe.r)