# Inference for Paired Data

by David Spade, PhD
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Foliensatz 8 Statstics II David Spade.pdf
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### About the Lecture

The lecture Inference for Paired Data by David Spade, PhD is from the course Statistics Part 2. It contains the following chapters:

• Inference for paired Data
• The Paired t-Test
• Pitfalls to Avoid

### Included Quiz Questions

1. Paired data refers to situations in which two measurements are taken on the same individual and the differences in the measurements are observed.
2. Paired data refers to data in which the measurements are taken on different individuals in each group.
3. With paired data, the data in each group are independent.
4. There is no difference between paired data and the type of data used for the two-sample t-test.
1. The differences can have any distribution, and the paired t-procedures will still work well regardless of the sample size.
2. The data must be paired.
3. The differences must be independent.
1. There is no difference between the paired t-test and the one-sample t-test after the differences are calculated because the differences can be viewed as a random sample from a single population.
2. The standard error is calculated differently for the differences than it is for the individual observations in the one-sample t-test.
3. The degrees of freedom for the test statistic are computed differently for the two tests.
4. The test statistic is calculated differently for the two tests.
1. There is no difference in the two confidence interval procedures because the differences can be viewed as a random sample from one population.
2. The degrees of freedom are different between the two procedures.
3. The standard error calculation is different between the two procedures.
4. The critical value is found differently between the two procedures.
1. It is important to examine side-by-side boxplots or histograms for differences when the data are paired.
2. It is important to be careful not to use a two-sample t-test with paired data.
3. It is important not to use the paired t -procedures when the data are not paired.
4. It is important to be cautions of outlying differences when working with paired data.

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