#HOW TO CALCULATE STANDARD ERROR FOR PROPORTION SERIES#
If we draw a series of samples and calculate the mean of the observations in each, we have a series of means. Thus the variation between samples depends partly also on the size of the sample. A consequence of this is that if two or more samples are drawn from a population, then the larger they are, the more likely they are to resemble each other - again, provided that the random sampling technique is followed. In other words, the more people that are included in a sample, the greater chance that the sample will accurately represent the population, provided that a random process is used to construct the sample. Furthermore, it is a matter of common observation that a small sample is a much less certain guide to the population from which it was drawn than a large sample. Thus the variation between samples depends partly on the amount of variation in the population from which they are drawn. For example, a series of samples of the body temperature of healthy people would show very little variation from one to another, but the variation between samples of the systolic blood pressure would be considerable. They will show chance variations from one to another, and the variation may be slight or considerable. Resource text Standard error of the meanĪ series of samples drawn from one population will not be identical.
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This section considers how precise these estimates may be. The earlier sections covered estimation of statistics. Learning objectives: You will learn about standard error of a mean, standard error of a proportion, reference ranges, and confidence intervals.