Answer :
The absolute value of the difference between the point estimate and the population parameter it estimates is sampling error.
What is absolute value?
The number's absolute value, which ignores orientation, indicates how far it is from zero on the number line. A number can never be negative in absolute terms.
Concepts which are used in the given problems:
- Standard error - It is a gauge of how closely a sample statistically approximates the entire population. Standard deviation in sampling distribution refers to standard error.
- Sampling error - Instead of studying the entire population, a sample is used to determine the sampling error. The distinction between sample statistics and population parameter estimation is what causes this.
- Precision - It is described as how closely two estimates from several samples agree with one another.
- Error of confidence - A statistic that indicates the degree of sampling error in a specific research is known as the error of confidence. Additionally, the margin of error indicates the percent by which the actual findings would deviate from the stated population figure.
- Point estimates - Point estimates refer to population parameters that are calculated using sampling statistics.
The incorrect options can be found below:
Because the precision is the estimate that is near from the various samples, the absolute magnitude of the difference between the point estimate and the population parameter would not be a precision. Additionally, it is not a standard error since a standard error is an error that is far beyond the range of the sample mean. However, it is not a confidence error since a confidence error is a measure of how far the findings would deviate from the population under consideration.
By using the concept of precision, standard error and error of confidence, the incorrect choices are discovered.
The correct choice may be found below:
Sampling error is the measurement of the distance or difference between the population parameter and the sample's point estimate. The sampling mistake is an inaccuracy that results from drawing conclusions about the sample rather than the population being studied.
The idea of sampling error is used to calculate the absolute value of the distinction between both the point estimate and the population parameter is predicts.
Thus, the absolute value of the difference between the point estimate and the population parameter it estimates is sampling error.
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