# Sampling: What It Is, Different Types, and How Auditors and Marketers Use It

> **Definition**
>
> Sampling is a statistical method that involves selecting a set number of random observations from a larger population for analysis.

## What Is Sampling?

Sampling is a statistical technique for efficiently analyzing large datasets by selecting a representative subset. Rather than analyzing an entire dataset, sampling analyzes a small portion so researchers can make conclusions about a larger population. This allows for informed decision-making without exhaustive data collection.

Businesses and finance often use sampling. For example, a company looking to evaluate customer satisfaction would survey a carefully selected group of customers rather than all of its customers.  

### Key Takeaways

*   Businesses and governments use sampling for market research, financial auditing, and employment statistics.
*   Many types of sampling methods exist, including random, stratified, cluster, systematic, and convenience, all of which are suited to specific situations.
*   Sampling helps companies make better decisions, from predicting customer behavior to identifying fraud.

![Sampling](https://www.investopedia.com/thmb/JMHV3Tx9RCy3d7FDKIJ4EKiQBJY=/1500x0/filters:no_upscale():max_bytes(150000):strip_icc()/Term-Definitions_Sampling_V1-5cbccd3e45974388b845747f4776f75c.jpg)

*— Investopedia / Crea Taylor*

## How Sampling Works

Sampling relies on the idea that a well-chosen subset can provide an accurate reflection of the larger population. If done right, sampling reduces the need for exhaustive data collection while still providing reliable conclusions.

The sampling process can be done by conducting the following steps:

1.  **[Define the population](https://www.investopedia.com/terms/p/population.asp):** Identify the group from which the sample will be taken, such as customers, transactions, or employees.
2.  **Choose a sampling method:** Different methods can be used depending on the study's objectives. For example, random sampling focuses on fairness, whereas systematic sampling uses regular intervals.
3.  **Determine the sample size:** The [sample size](https://www.investopedia.com/ask/answers/042915/what-percentage-population-do-you-need-representative-sample.asp) needs to be manageable and large enough to provide reliable results.
4.  **Collect data from the sample:** Collecting data can be done in various ways depending on the study, such as surveys, interviews, or records.
5.  **Analyze and interpret the data:** Once the data is retrieved, it needs to be interpreted using statistical tools and methods to come up with conclusions.  
    

## Uses of Sampling

Sampling is widely used across many industries. Businesses and organizations rely on it to make critical decisions, and it is particularly common in economic research. For example, government agencies, such as the [Bureau of Labor Statistics (BLS)](https://www.investopedia.com/terms/b/bls.asp), use sampling to assess employment trends.

Rather than sampling every single business and household in the U.S., the BLS relies on samples. The Current Employment Statistics program samples approximately 119,000 businesses and government agencies, covering about 629,000 work sites. This allows policymakers and economists to gauge job growth, wage trends, and industry shifts without input from every employer in the country.

The Current Population Survey samples 60,000 households to track changes in the labor market. It provides insights into [unemployment](https://www.investopedia.com/terms/u/unemployment.asp), workforce participation, and employment across various demographics. These statistics influence government policy and business hiring decisions.

In addition to economics, sampling is used in many other ways. Companies regularly conduct product testing on a sample of consumers before rolling out a new item to the larger public. This is done to gauge interest, issues, and the likely success of the product.

Rather than sifting through millions of records, sampling is used by financial institutions to audit transactions to detect fraud. Retailers use sampling to analyze purchasing patterns rather than tracking every purchase. This can help with estimating future demand and setting prices.

## Types of Sampling

Different sampling techniques can be used in different scenarios, depending on the parameters and goals of the study. The different methods of sampling are as follows:

### Random Sampling

[Random sampling](https://www.investopedia.com/ask/answers/042815/what-are-disadvantages-using-simple-random-sample-approximate-larger-population.asp) is often used in surveys and market research, ensuring that every constituent of a population has an equal chance of being selected. For example, a bank might randomly choose 1,000 customers to assess spending habits. Randomly choosing these customers helps reduce bias and is great for getting general results.  

### Stratified Sampling

[Stratified sampling](https://www.investopedia.com/terms/stratified_random_sampling.asp) breaks a population into different subgroups (strata) based on specific, shared characteristics. Samples are then chosen from each group. Stratified sampling is best used when a population is diverse.

For example, if a company wanted to determine employee satisfaction, it would make no sense to randomly choose employees because every job function is different, which will directly impact job satisfaction. Stratified sampling would first divide workers based on department and then pick samples from each. This ensures that the subgroups are properly represented.  

### Cluster Sampling

Cluster sampling selects entire groups rather than individuals. For example, a consulting agency evaluating a bank's different branch performances would select entire branches rather than the individuals in each branch.

While cluster sampling might sound similar to stratified sampling, there are differences. Cluster sampling randomly selects entire groups while stratified sampling selects a few individuals from all groups.  
  
The individuals in cluster groups are different whereas the individuals in stratified sampling subgroups are the same due to a shared characteristic. The primary goal of cluster sampling is to make it easier to gather data.  

> **Important**
>
> Even with a well-chosen sample, there's a risk of sampling errors, which are differences between the sample and the larger population. Choosing larger samples helps reduce sampling errors.

### Systematic Sampling

In [systematic sampling](https://www.investopedia.com/terms/s/systematic-sampling.asp), every nth item is chosen from a population at regular intervals. For example, if a company wanted to analyze 2,000 invoices from a total of 20,000, it would choose every 10th invoice for review after a random starting point.

Systematic sampling is similar to random sampling but is more structured and ensures even coverage of the larger population. However, systematic patterns in the data could lead to unintended bias. For example, if a retail company selects every seventh day and that consistently falls on a weekend, it may overrepresent high sales days, skewing the results.

### Convenience Sampling

Convenience sampling is just that, convenient. It is cost-effective but has a high chance of introducing bias and may not truly be representative. For example, if a retail store only selects customers who come in during lunch, they miss out on the insights from evaluating morning and evening shoppers.

## Importance of Sampling in Business and Finance

As discussed, sampling has many uses. In business in finance, it is applied in varied ways:

*   **Market research:** Companies use sampling to understand consumer preferences and predict demand. By analyzing a sample of their [target audiences](https://www.investopedia.com/terms/t/target-market.asp), companies can determine product fit, gauge the interest in new items, and refine marketing strategies.
*   **Financial auditing:** Auditors perform a detailed analysis of company financials and transactions. They can choose transaction samples to identify errors and fraud without having to check every single company transaction. A sample would still allow auditors to identify inconsistencies or patterns of inaccurate reporting.
*   **Quality control in manufacturing:** To ensure product [quality](https://www.investopedia.com/terms/q/quality-control.asp), manufacturers use sampling without having to inspect every item produced. If defects are found in the sample, fixes can be made before the entire batch is sent out. This helps ensure customer satisfaction and avoid costly recalls.  
    

## Example of Sampling

Company XYZ is a large retail company that wants to know the average amount customers spend when they visit the store. Rather than analyzing millions of transactions, XYZ selects a random sample of 1,000 purchases.

If the total population (amount of transactions) is two million, and the sample shows that the average purchase amount is $50, the company can use this to estimate customer spending habits. To be accurate, the sample must represent the entire population. This means it needs to account for variations, such as time of day and customer demographics.

If the sample was biased (say, only choosing weekend shoppers), the result would be a skewed representation of the company's customers and spending patterns. If the sample is an accurate representation of the larger population, Company XYZ can conclude that the average spend of its customers for every store visit is $50.

This information can help XYZ set pricing strategies, make marketing decisions, and improve [inventory management](https://www.investopedia.com/terms/i/inventory-management.asp), all without having to perform a costly and time-intensive analysis of all 2 million transactions.

## The Bottom Line

Sampling is a powerful tool that helps businesses, researchers, and organizations make informed decisions about populations without having to perform intensive analysis of large data sets. By choosing well-represented subsets, organizations can save time and resources while still gaining valuable insights.

When done correctly, sampling is accurate and efficient, providing a reliable snapshot of a larger population, which is used by market researchers, government agencies, financial institutions, and auditors to aid with business decisions and policymaking.  

> **Article Sources**
>
> 1.  U.S. Bureau of Labor Statistics. "[Monthly Employment Situation Report: Quick Guide to Methods and Measurement Issues](https://www.bls.gov/bls/empsitquickguide.htm)."