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SIMPLE RANDOM SAMPLING_stud

# SIMPLE RANDOM SAMPLING_stud - SIMPLE RANDOM SAMPLING_stud...

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SIMPLE RANDOM SAMPL ING_stud .doc Jan 25’10 THE SIMPLE RANDOM SAMPLE DEFINITION. Statistical inference is a procedure by which we reach a conclusion about a population on the basis of the information contained in a sample that has been drawn from that population . (RECALL: sample is a subset , or part of a population.) There are many kinds of samples that may be drawn from a population, but only scientific samples can be used for making valid inferences about a population. The simplest of them is the simple random sample . DEFINITION. If a sample of size n is drawn from a population of size N (N > n) in such a way that every element in the population has an equal chance of being chosen as a part of the sample, then such a sample is called a simple random sample . Correspondingly, every possible sample of size n has the same chance of being selected. (A size of the population (or a sample) is the number of entities in it). {In general, a procedure used to collect the sample data is called sampling plan or sample design [2].} The mechanics of drawing a sample in accordance with the above definition is called simple random sampling . The sampling may be performed with replacement , or without replacement . The sampling WITH replacement assumes that every member of a population is available at each draw. For example, let us consider the sampling which involves selecting (from the shelves in a hospital’s medical records department) a sample of [enumerated – M.V.] charts for collecting information of some kind about patients. The sampling with replacement assumes that we select a chart to be in the sample, record the data of interest, and return the chart to the shelf . In such a case, the “population” of charts remains the same, and every chart may be drawn again on some subsequent draw, and the same data about the same patient will again be recorded.

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