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Hypothesis Testing II The Two-Sample Case

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Introduction In this chapter, we will look at the difference between two separate populations As opposed to the difference between a sample and the population, which was Chapter 8 example: males and females; or people with no children compared with people with at least one child You cannot test all males and all females, so need to draw a random sample from the population Will want to find that the difference between the samples is real (statistically significant) rather than due to random chance

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Summary of Chapter Difference between two group’s means for large samples Difference between two group’s means for small samples Difference between two group’s proportions for large samples Will end the chapter with the limitations of hypothesis testing

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**Hypothesis Testing with Sample Means**

Large Samples

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Assumptions We need to assume that each sample is random, and also that the two samples are independent of each other When random samples are drawn in such a way that the selection of a case for one sample has no effect on the selection of cases for another sample, the samples are independent To satisfy this requirement, you may randomly select cases from one list of the population, then subdivide that sample according to the trait of interest

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More Assumptions In the two-sample case, the null is still a statement of “no difference”, but now we are saying that the two populations are “no different” from each other The null stated symbolically:

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Null Hypothesis We know that the means of our samples are different, but we are stating in the null that they are theoretically the same in the two populations If the test statistic falls in the critical region, we may conclude that the difference did not occur by random chance, and that there is a real difference between the two groups

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Test Statistic In this chapter, the test statistic will be the difference in sample means If sample size is large, meaning that the combined number of cases in the two samples is larger than 100, the sampling distribution of the differences in sample means will be normal in form and the standard normal curve can be used for critical regions Instead of plotting sample means or proportions in the sampling distribution, we will plot the difference between the means of each sample

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**Formula for Z (Obtained)**

The Formula:

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Revised Formula We do not know the means of the populations in this chapter—only know the means for the samples The expression for the difference in the population means is dropped from the equation because the expression equals zero—we assume in the null hypothesis that the values are the same

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**New Formula for Z (Obtained)**

The Formula:

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Pooled Estimate Use Formula 9.4 for the denominator if we do not know the population standard deviation (called the pooled estimate):

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Interpretation We are testing a hypothesis that women will be more supportive of gun control than men Need a statistical interpretation Know that there is a difference between the means of the two samples Are doing the test of hypothesis to see if the difference is large enough to justify the conclusion that it did not occur by random chance alone but reflects a significant difference between men and women on this issue We find that Z (obtained) is -2.80, and Z (critical) is plus or minus 1.96 So, can conclude that the difference did not occur by random chance The test statistic falls in the critical region, so it is unlikely that the null is true

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**Sociological Interpretation**

Begin by looking at which group has the lower mean For our groups, we find that men have a lower average score on the Support for Gun Control Scale, so are less supportive of gun control than women We know that men and women are different in terms of their support for gun control Why would this be true?

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**Hypothesis Testing with Sample Means**

Small Samples

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Distribution Cannot use the Z distribution for the sampling distribution of the difference between sample means Instead will use the t distribution to find the critical region for unlikely sample outcomes Will need to make two adjustments The degrees of freedom now will be (N1 + N2) - 2

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Second Assumption An additional assumption is that the variances of the populations of interest are equal We may assume equal population variances if the sample sizes are approximately equal If one sample is large, and the other is small, we cannot use this test

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**Formula for the Pooled Estimate**

Formula for the pooled estimate of the standard deviation of the sampling distribution is different for small samples than for large samples

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**Formula for t (obtained)**

It is the same as for Z (obtained):

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**Interpretation of the Results**

We are testing the hypothesis that people with children are happier than people without children Statistical interpretation: Will use a two-tailed test, since no direction has been predicted The test statistic falls in the critical region, so married people with no children and married people with at least one child are significantly different on the variable satisfaction with family life

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**Sociological Interpretation**

Begin by comparing the means Higher scores indicate greater satisfaction Who is in each sample? The samples were divided into respondents with no children and respondents with at least one child Find that the respondents with no children scored higher on this attitude scale They are more satisfied with family life We know this difference is not due to chance, but is a real difference It completely contradicts our hypothesis

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**Hypothesis Testing With Sample Proportions (Large Samples)**

The null hypothesis states that no significant difference exists between the populations from which the samples are drawn Will use the formulas for proportions when there is a percentage in the question

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**Formula for Z (obtained)**

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**The Limitations of Hypothesis Testing**

For All Tests of Hypothesis

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**Probability of Rejecting the Null**

The probability of rejecting the null is a function of four independent factors The size of the observed differences The greater the difference, the more likely we reject the null The alpha level The higher the alpha level, the greater the probability of rejecting the null hypothesis

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**Probability of Rejecting the Null**

The use of one- or two-tailed tests The use of the one-tailed test increases the probability of rejection of the null The size of the sample The value of all test statistics is directly proportional to sample size (not inversely proportional) The larger the sample, the higher the probability of rejecting the null hypothesis

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**Two things to Remember about Sample Size**

Larger samples are better approximations of the populations they represent, so decisions based on larger samples about rejecting or failing to reject the null, can be regarded as more trustworthy It shows the most significant limitation of hypothesis testing

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**Limitation of Hypothesis Testing**

Because a difference is statistically significant does not guarantee that it is important in any other sense Particularly with very large samples (N’s in excess of 1,000) where very small differences may be statistically significant Even with small samples, trivial differences may be statistically significant, since they represent differences in relation to the standard deviation of the population So, statistical significance is a necessary but not sufficient condition for theoretical importance Once a research result has been found to be significant, the researcher still faces the task of evaluating the results in terms of the theory that guides the inquiry

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Conclusion A difference between samples that is shown to be statistically significant may not be theoretically important, practically important, or sociologically important Logic will have to determine that And measures of association that show the strength of the association

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