Page 104 - DLIS401_METHODOLOGY_OF_RESEARCH_AND_STATISTICAL_TECHNIQUES

Unit 6: Sampling Techniques

• Accuracy: Accuracy refers to how close a sample statistic is to a population parameter. Notes
Thus, if you know that a sample mean is 99 and the true population mean is 100, you
can make a statement about the sample accuracy. For example, you might say the
sample mean is accurate to within 1 unit.
• Precision: Precision refers to how close estimates from different samples are to each
other. For example, the standard error is a measure of precision. When the standard
error is small, estimates from different samples will be close in value; and vice versa.
Precision is inversely related to standard error. When the standard error is small, sample
estimates are more precise; when the standard error is large, sample estimates are less
precise.
• Margin of error: The margin of error expresses the maximum expected difference between
the true population parameter and a sample estimate of that parameter. To be meaningful,
the margin of error should be qualified by a probability statement. For example, a pollster
might report that 50% of voters will choose the Democratic candidate. To indicate the
quality of the survey result, the pollster might add that the margin of error is +5%, with
a confidence level of 90%. This means that if the same sampling method were applied
to different samples, the true percentage of Democratic voters would fall within the
margin of error 90% of the time.
The margin of error is equal to half of the width of the confidence interval In a previous
lesson, the tutorial described how to construct a confidence interval.

6.4.2 Sample Design
A sample design can be described by two factors.
• Sampling method: Sampling method refers to the rules and procedures by which some
elements of the population are included in the sample.
• Estimator: The estimation process for calculating sample statistics is called the estimator.
Different sampling methods may use different estimators. For example, the formula for
computing a mean score with a simple random sample is different from the formula for
computing a mean score with a stratified sample. Similarly, the formula for the standard
error may vary from one sampling method to the next.
The “best” sample design depends on survey objectives and on survey resources. For example,
a researcher might select the most economical design that provides a desired level of precision.
Or, if the budget is limited, a researcher might choose the design that provides the greatest
precision without going over budget. Or other factors might guide the choice of sample
design.

Task Make a study on Sampling Techniques.

6.5 Summary

• Successful statistical practice is based on focused problem definition. In sampling, this
includes defining the population from which our sample is drawn. A population can be
defined as including all people or items with the characteristic one wishes to understand.
• In defining the frame, practical, economic, ethical, and technical issues need to be addressed.
The need to obtain timely results may prevent extending the frame far into the future.

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