A Sampling "Error" is not a mistake. It is simply the reduction in accuracy to be expected when one makes an
estimate based on a portion – a Sample – of the data in Population or Process. There are several types of Sampling Error.
Two types of Sampling Errors are described in terms of their Probabilities:
- p is the Probability of an Alpha Error, the Probability of a False Positive.
- β is the Probability of a Beta Error, the Probability of a False Negative
A third type, the
Margin of Error (MOE) is the width of an interval in the units of the data. It is half the width of a 2-sided Confidence Interval.
All three types of Sampling Error are reduced when the Sample Size is increased.
This makes intuitive sense, because a very small Sample is more likely to not be a good representative of the properties of the larger Population or Process. But, the values of Statistics calculated from a much larger Sample are likely to be much closer to the values of the corresponding Population or Process Parameters
For more on the statistical concepts mentioned here (
p, β, MOE, Confidence Intervals, Statistical Errors, Samples and Sampling), please see my book or my YouTube channel -- both are titled Statistics from A to Z -- Confusing Concepts Clarified.
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