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ANATOMY OF A PROPORTION __ There are instances when we are not interested in a single value (x), or a sample mean, x , but are interested in the Proportion of a ^ population (denoted p), or of a sample (denoted p ; p-hat), that possesses a specific characteristic of interest to us. An example for a population: Suppose there are 789,654 Peanut Butter M&M’s in a large container waiting to be bagged, of which 157,931 are yellow. Then the proportion of all yellow Peanut Butter M&M’s in this container is p 157931 .20 789654 The population proportion p is equal to the number of elements in the population with a specific characteristic (here, yellow in color), divided by the total number of elements in the population. An example for a sample: Now suppose a random sample of 240 Peanut Butter M&M’s is taken from this container and 52 of them are yellow. Then the sample proportion of yellow Peanut Butter M&M’s is ^ p 52 .22 240 The sample proportion p is equal to the number of elements in the sample with a specific characteristic (here, yellow in color), divided by the total number of elements in the sample. Why the difference (.20 versus .22)? Recall that there is no guarantee that a randomly drawn sample will exactly match the population parameter. That difference is due to sampling error, a natural part of the sampling process. Recall also that with a larger sample size our sample proportion would be closer to the true population proportion p.