Final answer:
When studying married couples or taking two measurements from the same individuals, we use matched pairs design in statistics. This involves dependent samples and typically focuses on the population mean, as opposed to independent samples which compare separate and unrelated groups.
Step-by-step explanation:
When conducting a poll of political opinions from both members of married couples, the groups in this study are considered matched pairs because the individuals are inherently linked through their marital relationship. Similarly, in a study where students' anxiety levels are measured at two different points in time, this is also an example of a matched pairs design because the two measurements are taken from the same individuals. Matched pairs refer to a scenario where two samples are dependent and are being tested on the population mean, as opposed to independent samples, where the sample values from one population have no relationship with sample values from another.
Matched or Paired Samples
In matched or paired sample tests, a few key characteristics are present:
Simple random sampling is used.
Sample sizes are often small.
This differs from independent samples where two separate groups are being compared, and the individuals within one group are not paired or matched with individuals in the other group. An example of a scenario with independent samples would be testing whether 70 percent of husbands pass their driver's test on the first attempt versus 65 percent of wives, looking to see if the proportions are equal.