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In statistics, the difference between dependent and independent samples refers to the type of data collection and the relationship between the datasets.
Dependent Samples:
Dependent samples are pairs of data where each element in one group has a connection or relationship with a specific element in the other group. The two samples are not independent of each other. Examples of dependent samples include repeated measurements on the same individuals or paired measurements, such as before-and-after comparisons.
Independent Samples:
Independent samples are groups of data where there are no fixed pairings or relationships between the elements. The data in one group does not directly influence the data in the other group. Examples of independent samples include measurements on different individuals, group comparisons, or comparisons between different conditions.
Example:
Suppose we are studying the effectiveness of a medication. If we test the same medication on the same group of individuals before and after treatment, it is considered dependent samples. However, if we compare the medication's effects in one group of patients with a placebo in another group, it is considered independent samples.