Sample size planning for the coefficient of variation from the accuracy in parameter estimation approach

Area of Science:

• Statistics
• Biostatistics
• Quantitative Research Methods

Background:

• Accurate parameter estimation is crucial in scientific research.
• The coefficient of variation (CV) is a key metric for comparing variability across datasets.
• Existing sample size planning methods may not adequately address the precision of CV estimates.

Purpose of the Study:

• To develop an accuracy in parameter estimation approach for sample size planning for the coefficient of variation (CV).
• To provide methods for ensuring a sufficiently narrow confidence interval for the population CV.
• To incorporate a desired level of assurance for achieving a narrow confidence interval.

Main Methods:

• Developed a sample size planning method based on achieving a narrow confidence interval width for the CV.
• Introduced a modification to include a specified probability of assurance for the confidence interval width.
• Generated tables of necessary sample sizes for various scenarios.

Main Results:

• The proposed methods enable researchers to determine sample sizes for precise CV estimation.
• The assurance modification allows for probabilistic guarantees on confidence interval width.
• Freely available computer routines are provided for easy implementation.

Conclusions:

• The developed approach enhances sample size planning for studies focusing on the coefficient of variation.
• Researchers can now plan studies with greater confidence in the precision of their CV estimates.
• The methods and tools facilitate more robust quantitative research designs.