Sampling Error – Definition

Cite this article as:"Sampling Error – Definition," in The Business Professor, updated September 17, 2019, last accessed August 6, 2020, https://thebusinessprofessor.com/lesson/sampling-error-definition/.

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Sampling Error Definition

A sampling error is a form of error that occurs when a sample or subset that does not represent a whole is used to represent a whole. In population analysis for instance, a sampling error is a statistical error that occurs when a sample is observed and used as a representation of the whole population, the results of the observation would be incorrect because it does not represent the whole population. When a subset or sample of a population is collected o represent the entire population, there is an error in the data collection process and this error extends to the results realized.

Here are some important things you should know about sampling error;

  • A sampling error is a statistical error that emanates from the selection of a sample that fails to represent the entire population of data.
  • Since the collection of data is faulted, the results realized from the sampling becomes invalid because it fails to represent the entire population.
  • Largely, sampling error occurs when an analyst selects a sample or subset of data that does not represent the entire data population.
  • A reduction or an increase in the number of samples selected for observation can cause sampling error.

A Little More on What is a Sampling Error

It is important that selecting a sample for analysis does not automatically cause sampling error, rather, it is when the sample selected fails to represent the entire data population that sampling error occurs. For instance, when samples are randomly selected or the selection is influenced by biases, a sampling error is bound to occur.

Analysts can prevent sampling errors by selecting samples or subsets of an entire data population that efficiently represents the entire data. For instance, if the samples selected are small and inadequate to represent the entire population, an analysts can increase the samples selected for adequate representation.

Examples of Sampling Errors

This illustration below is helpful for a better understanding of how sampling errors occur;

Company ABC airs a programme on a local channel for teenagers, this company wants to analyze the percentage of teenagers that watch the programme and picks even randomized samples or viewers between the hours of 10am and 5pm, sampling errors can occur due to this. Here are some important factors that can lead to the error. For instance, since the program is designed for teenagers, there is a high tendency that the program will have more viewers after school hours, selecting a sample from 10am-5pm fails to capture the entire populations of viewers that the show would have. It is important to know that error in selection is the major cause of error in the analysis.

Factoring in Non-Sampling Errors

Sampling, which is a process that involves the selection of samples for analysis can produce two types of errors, the sampling error and non-sampling error. Non-sampling errors are caused by humans and can be avoided to a large extent. Non-sampling errors can also occur when the survey is being done, for instance, if 10 groups are sampled in a population and one of the 10 groups does not suit the survey, if the analyst includes this group in the survey, this is an instance of non-sampling error.

References for “Sampling Error

https://en.wikipedia.org/wiki/Sampling_error

https://www.qualtrics.com › Research Core › Determining Sample Size

https://www.investopedia.com › Investing › Financial Analysis

https://businessjargons.com/sampling-error.html

https://stattrek.com/statistics/dictionary.aspx?definition=sampling_error

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