Definition
Discovery Sampling is a type of exploratory sampling technique used in statistics, particularly in the fields of auditing and quality control. Its main objective is to assure that the proportion of units with a specific attribute (e.g., error) does not exceed a predefined threshold within a given population. This method involves selecting random samples and determining the presence or absence of the attribute in question.
Key Determinations
To effectively use discovery sampling, the following three determinations are necessary:
- Size of Population: The total number of units in the group being examined.
- Minimum Unacceptable Error Rate: The threshold percentage of units with the attribute (or error) that is considered unacceptable.
- Confidence Level: The degree of certainty that the true error rate is below the unacceptable threshold.
Sampling Procedure
The sample size required for discovery sampling is derived using a sampling table, which accounts for the population size, desired confidence level, and the minimum unacceptable error rate. If none of the randomly selected samples show the attribute in question, the auditor or quality control analyst can conclude with a specified confidence that the actual error rate in the entire population is below the minimum unacceptable error rate.
Examples
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Auditing Financial Records: An auditor selects a random sample of transactions to check for discrepancies. If none of the sampled transactions has an error, the auditor can conclude with a high level of confidence that the error rate in all transactions is below the predefined threshold.
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Quality Control in Manufacturing: A quality control inspector samples a batch of manufactured goods to ensure defects are below an acceptable percentage. If no defects are found in the samples, it can be concluded that the defect rate is within the acceptable limit for the entire batch.
Frequently Asked Questions
What is the significance of the confidence level in discovery sampling?
The confidence level is a statistical measure that indicates how certain you can be that the true error rate in the population is below the predefined unacceptable threshold. Common confidence levels include 90%, 95%, and 99%.
How is sample size determined in discovery sampling?
Sample size is determined by using a sampling table that considers the population size, confidence level, and the minimum unacceptable error rate. These tables are often found in statistical textbooks and auditing guides.
What happens if errors are found in the samples?
If errors are found in the samples, it indicates that the actual error rate may exceed the minimum unacceptable error rate, thereby failing the discovery sampling criteria. Further investigation or a larger sample size may then be required.
Can discovery sampling be used in fields other than auditing and quality control?
Yes, discovery sampling can be applied in any field where there is a need to ensure that the prevalence of a particular attribute does not exceed an acceptable threshold. This could include healthcare, environmental studies, and any area where quality assurance is critical.
Is discovery sampling the same as attribute sampling?
While both are used to assess the presence of attributes in a population, discovery sampling specifically focuses on ensuring that the proportion of the attribute does not exceed a predefined limit, usually associated with auditing and quality control contexts.
Related Terms
- Attribute Sampling: A type of sampling that assesses whether each unit in a sample has a certain attribute or not.
- Error Rate: The proportion of units in a population that contain errors.
- Confidence Interval: A range of values, derived from a sample, that is likely to contain the true value of an unknown population parameter.
- Population: The complete set of items or people observed in a study.
Online References
- American Institute of CPAs (AICPA)
- National Institute of Standards and Technology (NIST) - Quality Control
- Audit and Assurance Services, by Alvin A. Arens, Randal J. Elder, Mark S. Beasley
Suggested Books for Further Studies
- “Auditing and Assurance Services” by Alvin A. Arens, Randal J. Elder, and Mark S. Beasley
- “Statistical Techniques in Business and Economics” by Douglas A. Lind, William G. Marchal, and Samuel A. Wathen
- “Principles of Quality Control” by Jerry Banks
- “Statistical Quality Control” by Douglas C. Montgomery
Fundamentals of Discovery Sampling: Statistics Basics Quiz
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