In statistics and data analysis, stratified random sampling is a method used to select a sample from a larger population by first dividing that population into smaller groups, called strata, and then randomly selecting observations from each group.
The purpose of stratified sampling is to ensure that important subgroups within a population are represented in the final sample.
Expert Assistance: Need a defensible sampling plan? Contact Praxis Reliability Consulting today to schedule a consultation with our statistical consulting team.
Basic Terminology
Population
The population is the complete group being studied. In litigation matters, a population may include:
- Employees within a company
- Product warranty claims
- Customer accounts
- Medical records
- Financial transactions
- Survey respondents
Sample
A sample is a smaller subset selected from the population for review or analysis.
Strata
Strata are the groups used to divide the population before sampling occurs. These groups are usually based on characteristics that are relevant to the matter being evaluated.
Examples of strata may include:
- Geographic region
- Department or job classification
- Age groups
- Product types
- Time periods
- Gender or race classifications
- Claim severity levels
Random Sampling
Within each stratum, observations are selected randomly. Random selection means that each item within a group has a known chance of being selected.
How Stratified Random Sampling Works
A simplified process generally involves the following steps:
- Define the overall population
- Divide the population into strata
- Determine how many observations to select from each group
- Randomly select observations within each stratum
- Combine the selected observations into the final sample
Why Stratification is Used
Stratification is often used when a population contains meaningful differences between groups. By separating the population into strata before sampling, the resulting sample may better reflect the structure of the overall population.
For example:
- A workforce may contain multiple departments with different employee counts
- A product line may contain several models with different field exposure
- A customer base may span multiple geographic regions
Without stratification, some groups may be underrepresented or omitted entirely in a simple random sample.
Stratified random sampling may also assist in evaluating whether responses or outcomes differ across meaningful groups within a population.
Applications in Litigation Matters
Stratified random sampling is commonly discussed in matters involving large volumes of records or data. Examples may include:
- Employment and labor matters
- Consumer class actions
- Product liability cases
- Warranty claim analyses
- Financial record reviews
- Healthcare record evaluations
- Survey-based studies
- Document review and auditing
In litigation matters, stratified sampling may be used when reviewing every record in a population would be impractical due to time, cost, or volume considerations.
Example: Employment Records
Suppose a company operates in five regions and employs several different job classifications. A stratified sample may divide employees by:
- Geographic region
- Department
- Job level
Random samples could then be selected from each group to ensure representation across the organization.
Example: Product Claims
In a warranty analysis, claims may be grouped by:
- Manufacturing period
- Product model
- Failure type
- Geographic market
Sampling from each group may help organize and structure the review process.
Evaluating Responses Across Different Groups
One reason stratified random sampling is used is to help evaluate whether responses, outcomes, or behaviors differ across defined groups within a population.
For example, in a litigation matter, questions may arise regarding whether:
- Employee outcomes differ by department or geographic region
- Customer responses vary by market segment
- Product performance differs across manufacturing periods
- Survey responses vary among demographic classifications
By organizing the population into strata before sampling, the analysis can separately examine observations within each group while still maintaining a structured overall sampling approach.
This framework may assist in identifying patterns, differences, or trends that could otherwise be difficult to observe if all observations were combined into a single undifferentiated sample.
Stratified Sampling vs. Simple Random Sampling
A simple random sample selects observations directly from the full population without first dividing the data into groups.
A stratified random sample first separates the population into strata and then performs random selection within each group.
The primary distinction is the use of predefined groups prior to sampling.
Common Allocation Approaches
There are different ways observations may be allocated across strata.
Proportional Allocation
The number of sampled observations from each group is proportional to the size of that group in the population.
Equal Allocation
The same number of observations is selected from each stratum, regardless of group size.
Targeted Allocation
Sampling quantities may be adjusted to focus on specific groups of interest within the population.
Important Considerations
Several practical considerations are commonly evaluated when designing a stratified sample:
- Definition of the population
- Selection of appropriate strata
- Sample size requirements
- Availability and quality of underlying data
- Consistency of classification methods
- Documentation of the sampling process
Clear documentation is often important in matters involving regulatory review, litigation, or expert analysis.
Final Thoughts
Stratified random sampling is a structured sampling method that organizes a population into groups before random selection occurs. The approach is commonly used in statistical analyses, auditing, survey design, and litigation-related data review where representation across different categories or classifications is relevant.
Need defensible statistical evidence? Contact Praxis Reliability Consulting today for expert assistance with your sampling and analysis needs.