The goodness-of-fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to draw a conclusion about whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence.
Note
The expected value for each cell needs to be at least five for you to use this test.
Hypotheses
H_{0}: The distributions of the two populations are the same.
H_{a}: The distributions of the two populations are not the same.
Test StatisticUse a ${\chi}^{2}$ test statistic. It is computed in the same way as the test for independence.
Degrees of freedom (df)df = number of columns – 1
RequirementsAll values in the table must be greater than or equal to five.
Common UsesComparing two populations. For example: men vs. women, before vs. after, east vs. west. The variable is categorical with more than two possible response values.
Example 11.8
Do male and female college students have the same distribution of living arrangements? Use a level of significance of 0.05. Suppose that 250 randomly selected male college students and 300 randomly selected female college students were asked about their living arrangements: dormitory, apartment, with parents, other. The results are shown in Table 11.19. Do male and female college students have the same distribution of living arrangements?
Dormitory | Apartment | With Parents | Other | |
Males | 72 | 84 | 49 | 45 |
Females | 91 | 86 | 88 | 35 |
H_{0}: The distribution of living arrangements for male college students is the same as the distribution of living arrangements for female college students.
H_{a}: The distribution of living arrangements for male college students is not the same as the distribution of living arrangements for female college students.
Degrees of freedom (df):
df = number of columns – 1 = 4 – 1 = 3
Distribution for the test: ${\chi}_{3}^{2}$
Calculate the test statistic: χ^{2} = 10.1287 (calculator or computer)
Probability statement: p-value = P(χ^{2} >10.1287) = 0.0175
Using the TI-83, 83+, 84, 84+ Calculator
MATRXkey and arrow over to
EDIT. Press
1:[A]. Press
2 ENTER 4 ENTER. Enter the table values by row. Press
ENTERafter each. Press
2nd QUIT. Press
STATand arrow over to
TESTS. Arrow down to
C:χ2-TEST. Press
ENTER. You should see
Observed:[A]and
Expected:[B]. Arrow down to
Calculate. Press
ENTER. The test statistic is 10.1287 and the p-value = 0.0175. Do the procedure a second time but arrow down to
Drawinstead of
Calculate.
Compare α and the p-value: Since no α is given, assume α = 0.05. p-value = 0.0175. α > p-value.
Make a decision: Since α > p-value, reject H_{0}. This means that the distributions are not the same.
Conclusion: At a 5 percent level of significance, from the data, there is sufficient evidence to conclude that the distributions of living arrangements for male and female college students are not the same.
Notice that the conclusion is only that the distributions are not the same. We cannot use the test for homogeneity to draw any conclusions about how they differ.
Do families and singles have the same distribution of cars? Suppose that 100 randomly selected families and 200 randomly selected singles were asked what type of car they drove: sport, sedan, hatchback, truck, van/SUV. The results are shown in Table 11.20. Do families and singles have the same distribution of cars? Test at a level of significance of 0.05.
Sport | Sedan | Hatchback | Truck | Van/SUV | |
---|---|---|---|---|---|
Family | 5 | 15 | 35 | 17 | 28 |
Single | 45 | 65 | 37 | 46 | 7 |
Example 11.9
Both before and after a recent earthquake, surveys were conducted asking voters which of the three candidates they planned on voting for in the upcoming city council election. Has there been a change since the earthquake? Use a level of significance of 0.05. Table 11.21 shows the results of the survey. Has there been a change in the distribution of voter preferences since the earthquake?
Perez | Chung | Stevens | |
Before | 167 | 128 | 135 |
After | 214 | 197 | 225 |
H_{0}: The distribution of voter preferences was the same before and after the earthquake.
H_{a}: The distribution of voter preferences was not the same before and after the earthquake.
Degrees of freedom (df):
df = number of columns – 1 = 3 – 1 = 2
Distribution for the test: ${\chi}_{2}^{2}$
Calculate the test statistic: χ^{2} = 3.2603 (calculator or computer)
Probability statement: p-value=P(χ^{2} > 3.2603) = 0.1959
Using the TI-83, 83+, 84, 84+ Calculator
Press the MATRX
key and arrow over to EDIT
. Press 1:[A]
. Press 2 ENTER 3 ENTER
. Enter the table values by row. Press ENTER
after each. Press 2nd QUIT
. Press STAT
and arrow over to TESTS
. Arrow down to C:χ2-TEST
. Press ENTER
. You should see Observed:[A]
and Expected:[B]
. Arrow down to Calculate
. Press ENTER
. The test statistic is 3.2603 and the p-value = 0.1959. Do the procedure a second time but arrow down to Draw
instead of Calculate
.
Compare α and the p-value: α = 0.05 and the p-value = 0.1959. α < p-value.
Make a decision: Since α < p-value, do not reject H_{o}.
Conclusion: At a 5 percent level of significance, from the data, there is insufficient evidence to conclude that the distribution of voter preferences was not the same before and after the earthquake.
Ivy League schools receive many applications, but only some can be accepted. At the schools listed in Table 11.22, two types of applications are accepted: regular and early decision.
Application Type Accepted | Brown | Columbia | Cornell | Dartmouth | Penn | Yale |
---|---|---|---|---|---|---|
Regular | 2,115 | 1,792 | 5,306 | 1,734 | 2,685 | 1,245 |
Early Decision | 577 | 627 | 1,228 | 444 | 1,195 | 761 |
We want to know if the number of regular applications accepted follows the same distribution as the number of early applications accepted. State the null and alternative hypotheses, the degrees of freedom and the test statistic, sketch the graph of the p-value, and draw a conclusion about the test of homogeneity.