STATISTICS FROM A TO Z<br />-- CONFUSING CONCEPTS CLARIFIED
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#Statistics Tip of the Week: Increase Sample Size to Reduce Sampling Errors

4/13/2017

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All other things being equal, an increase in Sample Size (n) reduces all types of Sampling Errors, including Alpha and Beta Errors and the Margin of Error.

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 p, see my video P, the p-value. In the future, there will also be videos on Alpha and Beta Error, the Margin of Error, and Confidence Intervals. You can subscribe to the channel to be notified.
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    Andrew A. (Andy) Jawlik is the author of the book, Statistics from A to Z -- Confusing Concepts Clarified, published by Wiley.

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  • Home
    • Why This Book Is Needed
    • Articles List, Additional Concepts
    • Examples: 1-Page Summaries
    • Examples: Concept Flow Diagram
    • Examples: Compare and Contrast Tables
    • Examples: Cartoons
    • Example: Which to Use When Article
  • Buy
  • Blog
  • Sample Articles
  • Videos
  • Author
  • Communicate
  • Files
  • Errata