Part of the total estimation error of a parameter (or alue of a property, such as concentration) caused by the random nature of the sample. ISO, 1977 RT sample, sampling.

The inaccuracy that arises because you interviewed one sample of the population rather than another equivalent sample. If the whole population is interviewed (see census), there can be no sampling error.

The error which arises because the data are collected from a part, rather than the whole, of the population. It is usually measurable from the sample data in the case of probability sampling.

the gap between statistics reported from the sample and the actual statistic in the population. A larger sample or higher response rate within a study will reduce sampling error and allow a researcher to be more confident that data collected from a sample better reflects the population.

The difference between results from the sample and the results that might be obtained from a complete census, inherent in the statistical processes of sampling. This margin of error does not imply a mistake, for probability sample error is calculable, based on sample size and method. Sampling error is also called variance, tolerance factor, or standard deviation.

The difference in results between what a small sample of people think and what the results would be if the entire population were surveyed.

The estimated inaccuracy of the results of a study when a population sample is used to explain the behavior of the total population.

Unless the auditor examines 100% of the population, there is some chance the sample results will mislead the auditor. This risk is sampling error. The larger the sample, the less chance of sampling error and the greater the reliability of the results.

The deviation between the characteristics of a sample and a population.

The degree to which the results from the sample deviate from those that would be obtained from the entire population, because of random error in the selection of respondent and the corresponding reduction in reliability (Alreck, 454).

The uncertainty in the estimates which arises from taking a random sample of the household population. The likely size of this error for a particular statistic can be identified and expressed as a confidence interval. For more information see Appendix 5.

The difference between the survey results obtained with a sample, and the results that would be obtained with a complete study of the entire population using the same procedures used for the sample.

Sampling error refers to the level of uncertainty surrounding a data point that is drawn from a sample of the relevant population.

Error that arises as a matter of chance in the process of selecting individuals for participation in a public opinion poll or other study.

the phenomenon in which chance deviations from expected proportions arise in small samples.

That part of the difference between a sample estimate and the true population value (derived from a census) which arises because of estimating from a sample rather than from the whole population. It represents the uncertainty due to sampling.

Inaccuracy in an estimate causee by variability among observations

An error derived from a mistake in sampling procedure.

The measure of sampling variability, that is, the variations that might occur by chance because only a sample of the population is surveyed. In other words, that part of the error of an estimate which is due to the fact that the estimate is obtained from a sample rather than from a census of the universe.

Errors that occur because only part of the population is directly contacted. With any sample, differences are likely to exist between the characteristics of the sampled population and the larger group from which the sample was chosen. Sampling error, unlike nonsampling error, is measurable.

In statistics, when analyzing collected data, the samples observed differ in such things as means and standard deviations from the population from which the sample is taken. This is sampling error and is controlled by ensuring that, as much as possible, the samples taken have no systematic characteristics and are a true random sample from all possible samples. If the observations are a true random sample, statistics can make probability estimates of the sampling error and allow the researcher to estimate what further experiments are necessary to minimize it.