Sampling is a process in statistical analysis where researchers take a predetermined number of observations from a larger population. Sampling allows researchers to conduct studies about a large group by using a small portion of the population. The method of sampling depends on the type of analysis being performed, but it may include simple random sampling or systematic sampling. Sampling is commonly done in statistics, psychology, and the financial industry.
Key Takeaways
- Sampling allows researchers to use a small group from a larger population to make observations and determinations.
- Types of sampling include random sampling, block sampling, judgment sampling, and systematic sampling.
- Researchers should be aware of sampling errors, which may be the result of random sampling or bias.
- Companies use sampling as a marketing tool to identify the needs and wants of their target market.
- Certified public accountants use sampling during audits to determine the accuracy and completeness of account balances.
Understanding How Sampling Works
It can be challenging for researchers to conduct accurate studies on large populations. Sometimes, it’s impossible to study every individual in the group. Researchers often choose a small portion to represent the entire group, known as a sample. By using characteristics of this small group, researchers make estimates of the larger population.
The chosen sample should be a fair representation of the entire population. When taking a sample from a larger population, it’s essential to properly select the sample. To get a representative sample, it should be drawn randomly and encompass the whole population. For example, a lottery system may determine the average age of students in a university by sampling 10% of the student body.
Sampling sees widespread use in economics. For instance, the monthly employment report involves sampling, as conducted by the U.S. Bureau of Labor Statistics (BLS):
- The Current Employment Statistics using 122,000 businesses and government agencies.
- The Current Population Survey with a sample of 60,000 different households across the country.
Researchers need to be aware of sampling errors, which can occur when the selected sample doesn’t represent the entire population, causing deviations in results. These errors may be random or biased. Some members might opt out, or the sample may have inherent differences from the larger population.
Although sampling isn’t an exact science, it provides generalizations valuable for broader population conclusions.
Exploring the Types of Sampling
Different types of sampling methods are available for researchers:
Random Sampling
With random sampling, every item in a population has an equal chance of being chosen. This method minimizes potential bias since human judgment isn’t involved. For instance, selecting names of 25 employees out of a hat in a company of 250 employees ensures an equal chance for each employee.
Judgment Sampling
Auditor judgment can be employed to select the sample from the full population, especially for transactions of a material nature. If an auditor sets a threshold for materiality for accounts payable transactions at $10,000, given a small population, an auditor may choose all relevant transactions. Though practical, this method opens doors to potential judgment biases.
Block Sampling
Block sampling includes selecting a consecutive series of items within the population as the sample. For instance, sorting sales transactions by date or amount falls under this category. This method is convenient but might not representative the entire population.
Systematic Sampling
Systematic sampling involves selecting items through a fixed, periodic interval from a randomly chosen starting point. For example, an auditor assessing checks over $10,000 may decide on a sampling interval, like picking every fifth transaction.
Real-World Examples of Sampling
Market Sampling
Businesses, aiming to sell products/services to specific target markets, identify market needs through sampling. Understanding the target audience’s needs ensures the development of fulfilling products/services.
Audit Sampling
During financial audits, certified public accountants (CPAs) employ sampling to ascertain the accuracy and completeness of large account balances. This is especially necessary when dealing with vast volumes of transaction information.
Tackling Sampling Errors
Sampling error denotes the skewness arising when the review sample doesn’t reflect the entire population, compromising the study’s validity. For example, including professors in a survey aiming to gauge student experiences leads to sampling errors, introducing bias or random variations.
Understanding Cluster Sampling
Cluster sampling divides the population into smaller groups, with random selections forming samples. Ideal for managing large populations and sample sizes, it’s instrumental in simplifying research operations.
Differentiating Probability and Non-Probability Sampling
Probability sampling introduces random sampling to foster unbiased, strong conclusions about a population. In contrast, non-probability sampling offers ease of information collection but tends to introduce bias due to its non-random nature.
Conclusion: Embrace the Power of Sampling
Statisticians leverage sampling to handle large populations efficiently. This technique taps a small group from a large pool, reigning prominent in surveys, economics, and statistical analyses. Sampling reduces the complexity of large-scale data analysis, paving the way for insightful research.
Related Terms: Relative Sampling, Sampling Bias, Statistic Analysis Errors.
References
- U.S. Bureau of Labor Statistics. “Monthly Employment Situation Report: Quick Guide to Methods and Measurement Issues”.
- American Institute of Certified Public Accountants. “Section 350 Audit Sampling”, Page 2067.
