Independent and dependent samples
An independent sample is selected randomly so that its observed values do not depend on the observed values of another sample. Many statistical analyses are based on the assumption that samples are independent. Others are designed to evaluate samples that are dependent.
For example, suppose quality inspectors want to compare two laboratories to see if their blood lead tests give similar results. They send blood samples drawn from the same 10 children to both labs for analysis.
|
Child |
Lab A |
Lab B |
|
1 |
0.8 |
0.7 |
|
2 |
4.8 |
5.0 |
|
3 |
7.9 |
7.8 |
|
4 |
15.7 |
16.3 |
|
5 |
21.2 |
20.2 |
|
6 |
9.7 |
9.4 |
|
7 |
38.7 |
44 |
|
8 |
5.1 |
5.1 |
|
9 |
29 |
26.9 |
|
10 |
75.2 |
74.6 |
Because both labs tested a blood specimen from the same child, the test results are dependent. To obtain independent samples, the inspectors would need to randomly select and test 10 children using Lab A and then randomly select and test another group of 10 different children using Lab B.