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When you select records randomly from a larger data set (or some master database), you can achieve the sampling in a few different ways, including: sampling without replacement, in which a subset of the observations are selected randomly, and once an observation is selected it cannot be selected again.
Without replacement: When sampling is done without replacement, each member of a population may be chosen only once. In this case, the probabilities for the second pick are affected by the result of the first pick.
In sampling without replacement, the two sample values aren't independent. Practically, this means that what we got on the for the first one affects what we can get for the second one. Mathematically, this means that the covariance between the two isn't zero. That complicates the computations.
Sampling with replacement will result in the same individual being chosen again whereas sampling without replacement will not. In order to account for this difference when using the table or generator for random digits, one must ignore the repeated number that was included in the labeling.
Sampling with replacement can be defined as random sampling that allows sampling units to occur more than once. Sampling with replacement consists of. A sampling unit (like a glass bead or a row of data) being randomly drawn from a population (like a jar of beads or a dataset).