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The need for independent samples

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Statistical tests are based on the assumption that each subject (or each experimental unit) was sampled independently of the rest. Data are independent when any random factor that causes a value to be too high or too low affects only that one value. If a random factor (one that you didn't account for in the analysis of the data) can affect more than one value, but not all of the values, then the data are not independent.

The concept of independence can be difficult to grasp. Consider the following three situations.

You are measuring blood pressure in animals. You have five animals in each group, and measure the blood pressure three times in each animal. You do not have 15 independent measurements. If one animal has higher blood pressure than the rest, all three measurements in that animal are likely to be high. You should average the three measurements in each animal. Now you have five mean values that are independent of each other.

You have done a biochemical experiment three times, each time in triplicate. You do not have nine independent values, as an error in preparing the reagents for one experiment could affect all three triplicates. If you average the triplicates, you do have three independent mean values.

You are doing a clinical study and recruit 10 patients from an inner-city hospital and 10 more patients from a suburban clinic. You have not independently sampled 20 subjects from one population. The data from the 10 inner-city patients may be more similar to each other than to the data from the suburban patients. You have sampled from two populations and need to account for that in your analysis.

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