Paired Samples

Definition

Paired samples refer to a type of experimental design where each subject or unit in the study is measured or observed under two different conditions or at two different time points. The key feature is that the measurements are paired, meaning they come from the same individual or unit under the different conditions.

5 Must Know Facts For Your Next Test

1. Paired samples are commonly used in studies where the goal is to measure the effect of an intervention or treatment on the same individuals.
2. The pairing of observations allows for the control of individual differences, as each participant serves as their own control, increasing the statistical power of the analysis.
3. Paired samples are often used in pre-post study designs, where measurements are taken before and after an intervention or treatment.
4. The analysis of paired samples typically involves the use of statistical tests that account for the dependence between the paired observations, such as the paired t-test or the Wilcoxon signed-rank test.
5. Paired samples can also be used in observational studies, where the goal is to compare two related measurements or characteristics within the same individuals.

Review Questions

• Explain the key feature of paired samples and how it differs from independent samples.
  • The key feature of paired samples is that the observations are related or matched, meaning they come from the same individual or unit under different conditions or at different time points. This is in contrast to independent samples, where the observations are unrelated and come from different individuals or units. The pairing of observations in paired samples allows for the control of individual differences, as each participant serves as their own control, increasing the statistical power of the analysis.
• Describe the types of study designs that commonly use paired samples and the advantages of this approach.
  • Paired samples are commonly used in pre-post study designs, where measurements are taken before and after an intervention or treatment. This allows researchers to measure the effect of the intervention on the same individuals, controlling for individual differences. Paired samples are also used in observational studies where the goal is to compare two related measurements or characteristics within the same individuals. The key advantage of paired samples is that they increase the statistical power of the analysis by accounting for the dependence between the paired observations, as each participant serves as their own control.
• Explain how the analysis of paired samples differs from the analysis of independent samples and the statistical tests typically used for each.
  • The analysis of paired samples typically involves the use of statistical tests that account for the dependence between the paired observations, such as the paired t-test or the Wilcoxon signed-rank test. These tests compare the mean or median difference between the paired observations, rather than comparing the means or medians of the two groups directly. In contrast, the analysis of independent samples involves the use of statistical tests that assume the observations are independent, such as the two-sample t-test or the Mann-Whitney U test. The choice of statistical test for paired samples is based on the assumptions of the data, such as the normality of the distribution of the differences between the paired observations.

Related terms

Dependent Samples:

Dependent samples, also known as paired samples or within-subjects samples, are a type of data where the observations are related or matched between the two groups or conditions being compared.

Repeated Measures:

Repeated measures refer to a study design where the same participants are measured or observed under different conditions or at multiple time points, resulting in paired or dependent data.

Matched Pairs:

Matched pairs are a type of paired sample where each observation in one group is paired with a corresponding observation in another group based on one or more matching characteristics, such as age, gender, or baseline measurements.