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Statistics LibreTexts

11.6: Test for Homogeneity

  • Page ID
    4615
  • 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 inside each cell needs to be at least five in order for you to use this test.

    Hypotheses

    \(H_0\): The distributions of the two populations are the same.

    Ha: The distributions of the two populations are not the same.

    Test Statistic

    Use a \(\chi^2\) test statistic. It is computed in the same way as the test for independence.

    Degrees of Freedom (\(\bf{df}\))

    \(df = \text{ number of columns }- 1\)

    Requirements

    All values in the table must be greater than or equal to five.

    Common Uses

    Comparing 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 11.11

    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 11.18. 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

    Table 11.18 Distribution of living arragements for college males and college females

    Answer

    Solution 11.11

    \(H_0\): 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 (\(\bf{df}\)):
    \(df =\text{ number of columns }– 1 = 4 – 1 = 3\)

    Distribution for the test:\(\chi_3^2\)

    Calculate the test statistic: \(\chi_c^2 = 10.129\)

    ...

    Figure 11.9


    The graph of the Chi-square shows the distribution and marks the critical value with three degrees of freedom at 95% level of confidence, \(\alpha = 0.05\), 7.815. The graph also marks the calculated \(\chi^2\) test statistic of 10.129. Comparing the test statistic with the critical value, as we have done with all other hypothesis tests, we reach the conclusion.

    Make a decision: Because the calculated test statistic is in the tail we cannot accept \(H_0\). This means that the distributions are not the same.

    Conclusion: At a 5% 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.

    Exercise 11.11

    Do families and singles have the same distribution of cars? Use a level of significance of 0.05. 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 11.19. 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

    Table 11.19

    Exercise 11.11

    Ivy League schools receive many applications, but only some can be accepted. At the schools listed in Table 11.20, 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

    Table 11.20

    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 \chi2 distribution and show the critical value and the calculated value of the test statistic, and draw a conclusion about the test of homogeneity.