Inference for Paired Data
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
What is meant by the term “paired data"?
- Paired data refers to situations in which two measurements are taken on the same individual and the differences in the measurements are observed.
- Paired data refers to data in which the measurements are taken on different individuals in each group.
- With paired data, the data in each group are independent.
- There is no difference between paired data and the type of data used for the two-sample t-test.
- Paired data must be collected at the same time.
What is not a condition necessary for a paired t-procedure?
- The differences can have any distribution, and the paired t-procedures will still work well regardless of the sample size.
- The data must be paired.
- The differences must be independent.
- Data must be collected from a random sample.
- Treatment groups must be randomly assigned.
Once the differences are computed, what is the difference between the one-sample t-test and the paired t-test?
- There is no difference between the paired t-test and the one-sample t-test after the differences are calculated as the differences can be viewed as a random sample from a single population.
- The standard error is calculated differently for the differences than it is for the individual observations in the one-sample t-test.
- The degrees of freedom for the test statistic are computed differently for the two tests.
- The test statistic is calculated differently for the two tests.
- There are no differences between the paired t-test and the one-sample t-test.
What is an example of a paired-t-test?
- Weighing patients before and after a weight-loss diet
- Weighing patients after a weight-loss diet
- Weighing patients after two weight-loss interventions
- Weighing two groups of patients before their primary care appointment
- Weighing patients on two different scales and comparing the difference
What is not something of which to be cautious for the paired t-procedures?
- It is important to examine side-by-side box plots or histograms for differences when the data are paired.
- It is important to be careful not to use a two-sample t-test with paired data.
- It is important not to use the paired t-procedures when the data are not paired.
- It is important to be cautious of outlying differences when working with paired data.
- It is important to make sure data is randomly sampled.
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Author of lecture Inference for Paired Data
David Spade, PhD
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