What is a 'Sampling Error'
A sampling error is a statistical error to which an analyst exposes a model simply because he or she is working with sample data rather than population or census data. Using sample data presents the risk that results found in an analysis do not represent the results that would be obtained from using data involving the entire population from which the sample was derived.
BREAKING DOWN 'Sampling Error'
The use of a sample relative to an entire population is often necessary for practical and/or monetary reasons. Although there are likely to be some differences between sample analysis results and population analysis results, the degree to which these can differ is not expected to be substantial.
Methods of reducing sampling error include increasing the sample size and ensuring that the sample adequately represents the entire population.

Sampling
A process used in statistical analysis in which a predetermined ... 
Representative Sample
A subset of a statistical population that accurately reflects ... 
Sampling Distribution
A probability distribution of a statistic obtained through a ... 
Sample
A subset containing the characteristics of a larger population. ... 
Systematic Sampling
A type of probability sampling method in which sample members ... 
Central Limit Theorem  CLT
A statistical theory that states that given a sufficiently large ...

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Standard error is a statistical term that measures the accuracy with which a sample represents a population. 
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How Does Sampling Work?
Sampling is a term used in statistics that describes methods of selecting a predefined representative number of data from a larger data population. 
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What is Systematic Sampling?
Systematic sampling is similar to random sampling, but it uses a pattern for the selection of the sample. 
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What is a Representative Sample?
In statistics, a representative sample accurately represents the makeup of various subgroups in an entire data pool. 
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Understanding the Simple Random Sample
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Central limit theorem is a fundamental concept in probability theory. 
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How to Use Stratified Random Sampling
Stratified random sampling is a technique best used with a sample population easily broken into distinct subgroups. Samples are then taken from each subgroup based on the ratio of the subgroupâ€™s ... 
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How can a representative sample lead to sampling bias?
Learn how using representative samples alone is not enough to make sampling bias negligible and why elements such as randomization ... Read Answer >> 
What percentage of the population do you need in a representative sample?
Learn about representative samples and how they are used in conjunction with other strategies to create useful data with ... Read Answer >> 
What's the difference between a representative sample and a convenience sample?
Learn the difference between convenience sampling and representative sampling and the advantages and disadvantages of each ... Read Answer >> 
What is the difference between systematic sampling and cluster sampling?
Learn about the differences between systematic sampling and cluster sampling, including how the samples are created for each ... Read Answer >> 
What's the difference between a representative sample and a random sample?
Explore the differences between representative samples and random samples, and discover how they are often used in tandem ... Read Answer >> 
What's the difference between a representative sample and an unbiased sample?
Discover how the different techniques used to select representative samples and unbiased samples are often exploited and ... Read Answer >>