Nirdeshik

1. T E S T F O R R A N D O M N E S S:
T H E R U N S T E S T

2. In this lecture, you will learn the following items:
• How to use a runs test to analyze a series of
events for randomness.
OBJECTIVE

3. INTRODUCTION
Every investor wishes he or she could predict the
behavior of a stock’s performance. Is there a pattern to a
stock’s gain/loss cycle or are the events random? One
could make a defensible argument to that question with
an analysis of randomness.
The runs test (sometimes called a Wald–Wolfowitz runs
test) is a statistical procedure for examining a series of
events for randomness. This nonparametric test has no
parametric equivalent. In this lecture, we will describe
how to perform and interpret a runs test for both small
samples and large samples.

4. THE RUNS TEST FOR RANDOMNESS
The runs test seeks to determine if a series of events
occur randomly or are merely due to chance. To
understand a run, consider a sequence represented by
two symbols, A and B. One simple example might be
several tosses of a coin where A = heads and B = tails.
Another example might be whether an animal chooses
to eat first or drink first. Use A = eat and B = drink.

5. The first steps are to list the events in sequential order
and count the number of runs. A run is a sequence of
the same event written one or more times. For
example, compare two event sequences. The first
sequence is written AAAAAABBBBBB.
Then, separate the sequence into same groups as
shown in Figure 1. There are two runs in this example,
R = 2. This is a trend pattern in which events are
clustered and it does not represent random behavior.

6. Consider a second event sequence written
ABABABABABAB. Again, separate the events into
same groups (see Fig. 2) to determine the number of
runs. There are 12 runs in this example, R = 12.
This is a cyclical pattern and does not represent random
behavior either. As illustrated in the two examples
earlier, too few or too many runs lack randomness.

7. A run can also describe how a sequence of events
occurs in relation to a custom value. Use two symbols,
such as A and B, to define whether an event exceeds or
falls below the custom value. A simple example may
reference the freezing point of water where A =
temperatures above 0°C and B = temperatures below
0°C.
In this example, simply list the events in order and
determine the number of runs as described earlier.

8. After the number of runs is determined, it must be
examined for significance.
We may use a table of critical values (see Table B.10).
However, if the numbers of values in each sample, n1
or n2, exceed those available from the table, then a
large sample approximation may be performed.

9. Example 1
Runs Test (Small Data Samples)
The following study seeks to examine gender bias in
science instruction. A male science teacher was
observed during a typical class discussion.
The observer noted the gender of the student that the
teacher called on to answer a question. In the course of
15 min, the teacher called on 10 males and 10 females.
The observer noticed that the science teacher called on
equal numbers of males and females, but he wanted to
examine the data for a pattern.

10. To determine if the teacher used a random order to
call on students with regard to gender, he used a runs
test for randomness.
Using M for male and F for female, the sequence of
student recognition by the teacher is:
MFFMFMFMFFMFFFMMFMMM.

11. 1. State the Null and Research Hypotheses
The null hypothesis states that the sequence of events is
random. The research hypothesis states that the
sequence of events is not random.
The null hypothesis is
H0: The sequence in which the teacher calls on males
and females is random.
The research hypothesis is
HA: The sequence in which the teacher calls on males
and females is not random.

13. 3. Choose the Appropriate Test Statistic
The observer is examining the data for randomness.
Therefore, he is using a runs test for randomness.

14. Compute the Test Statistic
First, determine the number of runs, R. It is helpful to
separate the events as shown in Figure 3. The number
of runs in the sequence is R = 13.

15. 5. Determine the Value Needed for Rejection of the
Null Hypothesis Using the Appropriate Table of
Critical Values for the Particular Statistic
Since the sample sizes are small, we refer to Table
B.10, which lists the critical values for the runs test.
There were 10 males (n1) and 10 females (n2). The
critical values are found on the table at the point for
n1 = 10 and n2 = 10.
We set = 0.05. The critical region for the runs test is 6
< R < 16. If the number of runs, R, is 6 or less, or 16
or greater, we reject our null hypothesis.

16. 6. Compare the Obtained Value with the Critical
Value
We found that R = 13. This value is within our critical
region (6 < R < 16).
Therefore, we do not reject the null hypothesis.

17. 7. Interpret the Results
We did not reject the null hypothesis, suggesting that
the sequence of events is random. Therefore, we can
state that the order in which the science teacher calls
on males and females is random.

18. 8 Reporting the Results
The reporting of results for the runs test should
include such information as the sample sizes for each
group, the number of runs, and the p-value with
respect to .
For this example, the runs test indicated that the
sequence was random (R = 13, n1 = 10, n2 = 10, p >
0.05). Therefore, the study provides evidence that the
science teacher was demonstrating no gender bias.

19. Example 2
Runs Test Referencing a Custom Value
The science teacher in the earlier example
wishes to examine the pattern of an “at-risk”
student’s weekly quiz performance. A passing
quiz score is 70. Sometimes the student failed
and other times he passed. The teacher wished
to determine if the student’s performance is
random or not. Table 1 shows the student’s
weekly quiz scores for a 12-week period.

26. SUMMARY
The runs test is a statistical procedure for examining a
series of events for randomness. This nonparametric
test has no parametric equivalent. In this lecture, we
described how to perform and interpret a runs test for
both small samples and large samples.