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Statistics LibreTexts

12.2: Sampling Error

  • Page ID
    8784
  • Regardless of how representative our sample is, it’s likely that the statistic that we compute from the sample is going to differ at least slightly from the population parameter. We refer to this as sampling error. The value of our statistical estimate will also vary from sample to sample; we refer to this distribution of our statistic across samples as the sampling distribution.

    Sampling error is directly related to the quality of our measurement of the population. Clearly we want the estimates obtained from our sample to be as close as possible to the true value of the population parameter. However, even if our statistic is unbiased (that is, in the long run we expect it to have the same value as the population parameter), the value for any particular estimate will differ from the population estimate, and those differences will be greater when the sampling error is greater. Thus, reducing sampling error is an important step towards better measurement.

    We will use the NHANES dataset as an example; we are going to assume that the NHANES dataset is the entire population, and then we will draw random samples from this population. We will have more to say in the next chapter about exactly how the generation of “random” samples works in a computer.

    In this example, we know the adult population mean (168.35) and standard deviation (10.16) for height because we are assuming that the NHANES dataset is the population. Now let’s take a few samples of 50 individuals from the NHANES population, and look at the resulting statistics.

    Table 12.1: Example means and standard deviations for several samples of Height variable from NARPS
    sampleMean sampleSD
    166 11.3
    168 9.5
    167 9.1
    167 9.8
    167 10.5

    The sample mean and standard deviation are similar but not exactly equal to the population values. Now let’s take a large number of samples of 50 individuals, compute the mean for each sample, and look at the resulting sampling distribution of means. We have to decide how many samples to take in order to do a good job of estimating the sampling distribution – in this case, let’s take 5000 samples so that we are really confident in the answer. Note that simulations like this one can sometimes take a few minutes to run, and might make your computer huff and puff. The histogram in Figure 12.1 shows that the means estimated for each of the samples of 50 individuals vary somewhat, but that overall they are centered around the population mean. The average of the 5000 sample means (168.38) is very close to the true population mean (168.35).

    The blue histogram shows the sampling distribution of the mean over 5000 random samples from the NHANES dataset.  The histogram for the full dataset is shown in gray for reference.
    Figure 12.1: The blue histogram shows the sampling distribution of the mean over 5000 random samples from the NHANES dataset. The histogram for the full dataset is shown in gray for reference.