Sampling > Sampling Variability
What is sampling variability?
Sampling variability is how much an estimate varies between samples. “Variability” is another name for range; Variability between samples indicates the range of values differs between samples.
Sampling variability is often written in terms of a statistic. The variance (σ2) and standard deviation (σ) are common measures of variability. You might also see reference to the variability of the sample mean (μ), which is just another way of saying the sample mean differs from sample to sample. Sampling variability only refers to a statistic (i.e. a number generated from a sample)—never a population.
Variability and Sampling Error
A closely related term (almost a synonym) is sampling error. An error in sampling isn’t a mistake — it’s a measure of how much a value differs from the “true” value. Let’s say the true weight of a population is 150 lbs. You take a sample and find the mean weight for the sample is 151 lbs. The 1 lb difference is an “error.” If you sample again, you might get different mean weights of 148 lbs, or 150.5 lbs, or 153 lbs. The different errors — 1/2 lb, 1 lb, 2 lbs, 3 lbs — are a reflection of the variability between your samples, or sampling variability.
Variability and Sample Sizes
Increasing or decreasing sample sizes leads to changes in the variability of samples. For example, a sample size of 10 people taken from the same population of 1,000 will very likely give you a very different result than a sample size of 100.
There is no “perfect” sample size that will give you accurate estimates for the sample mean, variance and other statistics. Instead, you take your best “guess” — using standardized statistical procedures (see: Finding the sample size). In general, estimates will change from sample to sample and will probably never exactly match the population parameter.
Next: Sampling Distributions.
Lodico, G. et al. (2010). Methods in Educational Research: From Theory to Practice.