1. Hypothesis Testing II
The Two-Sample Case

2. 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

3. 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

4. Hypothesis Testing with Sample Means
Large Samples

5. 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

6. 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:

7. 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

8. 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

9. Formula for Z (Obtained)
The Formula:

10. 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

11. New Formula for Z (Obtained)
The Formula:

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

13. 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

14. 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?

15. Hypothesis Testing with Sample Means
Small Samples

16. 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

17. 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

18. 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

19. Formula for t (obtained)
It is the same as for Z (obtained):

20. 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

21. 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

22. 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

23. Formula for Z (obtained)

24. The Limitations of Hypothesis Testing
For All Tests of Hypothesis

25. 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

26. 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

27. 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

28. 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

29. 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