Let’s know what is sampling error.When the sample mean is used as a point estimate of the population mean, then we can expect some error can be expected owing to the fact that a subset, or sample of the population,is used to compute the point estimate.

The absolute value of the difference between the sample mean denoted as x̄, and the population mean is denoted by μ, written as |x̄ − μ|, is known as the sampling error.

Probability statements about the magnitude of the sampling error can be incorporated by Interval estimation.

The sampling distribution of x̄ basically provides the basis for such a statement.

In this article we are going to discuss what is sampling error,sampling and sampling error, sampling error definition, sampling error formula and sampling error examples.

A sampling error can be defined as a statistical error that occurs when a sample that represents the entire population of data is not selected by an analyst and the results we find in the sample do not represent the actual results that can be obtained from the entire population.

We can define sampling as an analysis performed by selecting a number of observations generally from a larger population, and this selection produces both sampling and non-sampling errors.

Sampling error can be defined as a statistical error that generally occurs when an analyst does not select a sample that represents the entire population of data and selects some part of the data.

The results found in the sample do not represent the results which can be obtained from the entire population.

This error can be reduced by randomizing sample selection or by increasing the number of observations.

Let’s know the sampling error meaning. It can be defined as a deviation in sampled value versus the true population value due to the fact the sample selected is not a representative of the population or biased in any way. Even the randomized samples will have some sampling error as it is only an approximation of the population from which it is drawn.

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As has been illustrated above, the bigger is the sample size, the smaller will be the sampling error. The sampling error increases in proportion to the square root of the sample size that is denoted by n. For example, when we increase the sample size from 10 to 100, the sampling error halves, all else being equal.

The Formula for Sampling Error refers to the formula that's utilized in order to calculate statistical error that happens within the situation where person conducting the test doesn’t select sample that represents the entire population into account and as per the formula sampling error is calculated by dividing the quality deviation of the population by the root of the dimensions of sample then multiplying the resultant with the Z score value which is predicated on confidence interval.

Sampling Error = Z × (\[\frac{\sigma}{\sqrt{n}}\])

Where,

Z score value based on the confidence interval

σ denotes the population standard deviation

n denotes the sample size

Step 1) Gather all sets of knowledge called the population. Compute the population means and population variance .

Step 2) Now, one must determine the dimensions of the sample, and further the sample size has got to be but the population and it shouldn't be greater.

Step 3) Now you need to determine the confidence level and accordingly one can determine the value of the Z score from its table.

Step 4) Now multiply Z score by the population variance and divide an equivalent by the root of the sample size so as to reach a margin of error or sample size error.

Here are the steps for minimizing and controlling Sampling Error-

You can simply increase the sample size. A larger sample size generally leads to a more precise result because the study gets closer to the actual population size and the results obtained are more accurate.

Dividing the population into groups.

Important to know your population.

Random selection results in the elimination of bias.

You can train your team.

Performing an external record check.

Careful sample designs.

Take large enough samples.

Sampling error example 1) Suppose that the population standard deviation given is 0.40 and the size of the sample is equal to 2500 then find the sampling error at confidence level equal to 95%.

Solution) Let’s list down the data,

σ is equal to 0.40

Sample size (n) = 2500

The value of z at 95% of confidence level is equal to 1.96

Formula of Sampling error = Z × \[\frac{\sigma}{\sqrt{n}}\]

= \[\frac{0.40}{\sqrt{2500}}\times 1.96\]

= \[\frac{0.40}{50}\times 1.96 = 0.01568\]

Sampling error example 2 ) Find the sampling error of the sample size equal to 100 of the population with a standard deviation equal to 0.5 at 90% confidence level.

Answer)From the given data,

σ is equal to 0.5

Sample size (n) = 100

The value of z at 90% of confidence level is equal to 1.645

Formula of sampling error = Z × \[\frac{\sigma}{\sqrt{n}}\]

= \[\frac{0.5}{\sqrt{100}}\times 1.645\]

= \[\frac{0.5}{10}\times 1.645 = 0.08225\].

Note: Z-value at 90% confidence level is equal to 1.64.

FAQ (Frequently Asked Questions)

Question 1)What is the Sampling Error with Example? What are the Steps to Reduce Sampling Error?

Answer) Sampling error meaning.Sampling error can be defined as the difference between a population parameter and a sample statistic that is used to estimate it. Let’s take for example, the difference between a population mean and a sample mean is known to be the sampling error. Sampling error generally occurs because a portion, and not the entire population, is surveyed.

Here are the steps for minimizing Sampling Error

You can simply increase the sample size. A larger sample size generally leads to a more precise result because the study gets closer to the actual population size and the results obtained are more accurate.

Dividing the population into groups.

Important to know your population.

Random selection results in the elimination of bias.

You can train your team.

Performing an external record check.

Question 2 )Is Sampling Error Always Present?What are the Sources of Sampling Error?

Answer) Sampling Error is generally unavoidable.An estimate of sampling error is the margin of error that you commonly see with survey results. Because the sampling error is just an estimate, there is a small chance (typically five percent or less chance) that the margin of error is actually larger than stated in the report.

Sampling error generally occurs because survey information is observed from only a sample of the target population instead of from the entire population in general,increasing the size of the sample data basically leads to a decrease in the sampling error.