As an expert in the field of statistics, I can provide a comprehensive definition of sampling variation.
Sampling variation is a fundamental concept that describes the differences in outcomes that can occur when different samples are taken from the same population. It is a crucial aspect to understand when conducting statistical analyses because it affects the reliability and generalizability of the results.

In statistics, a population is the entire group of individuals or objects that we are interested in studying. A sample, on the other hand, is a subset of the population that is used to make inferences about the entire population. The process of drawing a sample from a population is known as sampling.

When we take a sample from a population, we calculate various statistics such as the mean, median, mode, and variance. These statistics give us a snapshot of the sample's characteristics. However, because a sample is only a part of the population, the statistics calculated from the sample are subject to change if we were to take a different sample from the same population. This changeability is what we refer to as sampling variation.

The sample variance is a specific type of sampling variation that refers to the variability of observations within a single sample. It is calculated as the average of the squared differences from the mean. The sample variance is a measure of how spread out the data points are in the sample.

Now, when we talk about sampling variance in a broader sense, we are referring to the variability of a particular statistic, such as the mean, that is calculated from a sample. If we were to repeat the study multiple times, taking different samples each time and calculating the statistic for each sample, we would observe differences in the values of the statistic. This variability in the statistic across different samples is known as sampling variance.

It's important to note that sampling variation is not a flaw or error in the study; it is a natural consequence of sampling. It is also not the same as sampling error, which refers to the difference between the sample statistic and the true population parameter. Sampling error can be reduced by increasing the sample size, but sampling variation cannot be eliminated because it is inherent to the sampling process.

Understanding sampling variation is essential for interpreting the results of statistical analyses. It helps us to understand the uncertainty associated with our estimates and to make more informed decisions when drawing conclusions from our data.

In summary, sampling variation is the variability we expect to see in statistics calculated from different samples taken from the same population. It is a natural part of the sampling process and is influenced by the size of the sample and the heterogeneity of the population. Recognizing and accounting for sampling variation is critical for the accurate interpretation of statistical results.

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Sample variance refers to variation of observations (the data points) in a single sample. Sampling variance refers to variation of a particular statistic (e.g. the mean) calculated in sample, if to repeat the study (sample-creation/data-collection/statistic-calculation) many times.Oct 14, 2011read more >>