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Stratified Random Sampling

Definition

Stratified random sampling is a method used in statistics and investing where a population is divided into subgroups, or strata, and random samples are then selected from each stratum. This approach is made to achieve a higher level of accuracy or representativeness in the sample selection. The technique is commonly used in mutual and index funds to construct a representative portfolio.

Phonetic

Stratified Random Sampling is pronounced as: stra-tuh-fahyd ran-duhm sam-pling

Key Takeaways

1. Characteristics of Population: Stratified Random Sampling ensures that the subgroups of a population are adequately represented within the entire sample population of a research study. This method divides the population into non-overlapping groups or ‘strata’ based on shared characteristics, such as age, gender, nationality, etc.

2. Increased Precision: Stratified Random Sampling enhances precision and accuracy in estimation. It reduces sampling error and potential bias by ensuring every subgroup is represented in the sample. This results in greater statistical efficiency than simple random sampling.

3. Needs Known Proportions: For Stratified Random Sampling to be effective, the researcher must know the proportional makeup of the population and the strata. An inaccurate understanding of the population can lead to misrepresentation and loss of accuracy in the results.

Importance

Stratified random sampling is important in business/finance as it enhances the accuracy and reliability of statistical results. The method involves dividing a population into subgroups or strata based on relevant characteristics such as age, income, or region then selecting random samples from each subgroup. This allows for a more accurate reflection of the overall population which improves the validity of the statistical analysis. It ensures that all relevant subgroups are represented proportionately and prevents any particular subgroup from dominating the sample. Therefore, stratified random sampling can help businesses make more informed decisions, minimize risks, and effectively target marketing strategies.

Explanation

Stratified Random Sampling is primarily employed to enhance the accuracy and robustness of the results of a study or survey. It’s a crucial methodology for eliminating bias and ensuring that diverse subsets of a population are adequately covered. In business or finance realms, this form of sampling is pivotal in gaining a comprehensive snapshot of a large population, for instance, a market or target consumer base. The stratification ensures representation from all pertinent sub-groups within the population, which can provide comprehensive insights into the market behavior and thereby, assist in informed decision-making.Moreover, Stratified Random Sampling is frequently employed in financial auditing. Auditors utilize this technique to assess financial transactions or balances. By creating strata based on categories like dollar amounts or transaction types, auditors can focus more on substantial areas or riskier transactions. For instance, in an audit of a company’s accounts receivable, stratification might involve categorizing receivables based on their ages. It allows a detailed understanding of different strata and identifies patterns or discrepancies more effectively hence ensuring thorough audit and superior control over risk.

Examples

1. Market Research: Suppose a car manufacturing company is planning to launch a new model. Before making the launch, they want to gauge the interest level across various age groups and income groups. They can use stratified random sampling to divide their potential consumers into different strata based on age and income. After segmentation, a number of people from each strata can be randomly selected for conducting a survey, helping them to get a fair representation from all groups.2. Audit Processes: An auditing firm is auditing a large multinational corporation and wants to ensure the accuracy of financial transactions. They might use stratified random sampling to divide the transactions into distinct groups, such as transactions under $1000, between $1000-$5000, and over $5000. The firm would then select a randomized sample from each group to get an accurate understanding of the overall financial status of the company.3. Healthcare Policy Planning: If a health department of a state wants to study the prevalence of a certain disease, such as diabetes, among different population groups, they might use stratified random sampling. They can divide the entire population into various strata based on factors such as age, gender, income level, ethnicity, etc. Selecting a random sample from each stratum, they can study the prevalence of the disease in each group, contributing to more efficient health policy planning.

Frequently Asked Questions(FAQ)

What is Stratified Random Sampling in finance and business term?

Stratified Random Sampling is a method of sampling that involves the division of a population into smaller groups known as strata. The strata are formed based on the members’ shared attributes or characteristics. A sample from each stratum is then taken in a number such that the proportionality of the stratum’s size in the total population is preserved.

Why is Stratified Random Sampling used?

It’s used when the researcher expects significant differences within a population or when the population itself is large. It increases the statistical precision of the analysis, reduces sampling error and provides a better representation of the population.

What is an example of Stratified Random Sampling in business?

Imagine a beverage company wants to survey its customers’ tastes in 5 different cities. The company might use Stratified Random Sampling to draw a random sample from each city (each city being its own stratum), ensuring the final sample reflects the population in each area.

How is Stratified Random Sampling different from Simple Random Sampling?

In Simple Random Sampling, each member of a population has an equal chance of being chosen without considering any characteristic. In Stratified Random Sampling, the population is divided into strata based on certain characteristics, and then members are randomly selected from each stratum.

What are the advantages of Stratified Random Sampling?

It ensures representation from all groups in a population, reduces sampling errors, and boosts precision, making it possible to draw more accurate inferences about a population.

What are the limitations of Stratified Random Sampling?

This method requires a clear understanding and division of the population into relevant strata. Incorrect division can lead to selection bias. Moreover, it can be time-consuming and expensive if strata are geographically dispersed.

Is Stratified Random Sampling used in financial audit?

Yes. Auditors often use this method to review representative samples of different strata or groups within a company’s financial data. These could be transactions of different sizes, different periods, or transactions related to different departments.

Can Stratified Random Sampling be used in market research?

Absolutely. Stratified Random Sampling can be used in market research to ensure that your sample accurately reflects the demographics of your target market, which in turn increases the validity of your research results.

Related Finance Terms

  • Population: The total set of observations that can be made.
  • Strata: Subgroups or divisions of a larger population where each subgroup shares at least one common characteristic.
  • Random Selection: The process of choosing the sample in such a way that every possible sample has an equal chance of being selected.
  • Sampling Error: The difference between the results from the sample and the actual value for the population.
  • Sample Size: The number of observations used in the sample from the population.

Sources for More Information

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