Stratified Sampling is a statistical technique used in the Analyze Phase of Lean Six Sigma to ensure that specific subgroups within a population are adequately represented in a sample. This method divides the entire population into distinct, non-overlapping groups called strata based on shared char…Stratified Sampling is a statistical technique used in the Analyze Phase of Lean Six Sigma to ensure that specific subgroups within a population are adequately represented in a sample. This method divides the entire population into distinct, non-overlapping groups called strata based on shared characteristics such as age, location, product type, shift, or machine used.
The primary purpose of stratified sampling is to reduce sampling error and increase the precision of estimates when analyzing data. By ensuring each stratum is proportionally represented, analysts can draw more accurate conclusions about the overall population and identify variations between different subgroups.
The process involves several key steps. First, identify the population and determine relevant stratification criteria based on factors that may influence the outcome being studied. Second, divide the population into mutually exclusive strata. Third, determine the sample size for each stratum, either proportionally based on stratum size or equally across all strata depending on analytical needs. Finally, randomly select samples from each stratum.
In Lean Six Sigma projects, stratified sampling proves particularly valuable when investigating process variations. For example, if a manufacturing facility operates three shifts and defect rates appear to vary, stratified sampling ensures data collection from all shifts proportionally, enabling proper comparison and root cause analysis.
Key benefits include improved accuracy of statistical analysis, better representation of minority subgroups, and the ability to make valid comparisons between strata. This technique helps Green Belt practitioners identify whether certain factors or conditions contribute to quality issues or process inefficiencies.
Compared to simple random sampling, stratified sampling provides more reliable insights when population heterogeneity exists. It ensures that important subgroups are not underrepresented or overlooked during data collection, leading to more robust conclusions and better-informed decisions during the Analyze Phase of DMAIC methodology.
Stratified Sampling: A Comprehensive Guide for Six Sigma Green Belt
What is Stratified Sampling?
Stratified sampling is a probability sampling technique where the population is divided into distinct subgroups called strata based on shared characteristics. Samples are then drawn from each stratum in proportion to their representation in the overall population. This ensures that each subgroup is adequately represented in the final sample.
Why is Stratified Sampling Important?
In the Analyze Phase of Six Sigma, stratified sampling is crucial because it:
• Reduces sampling error by ensuring all relevant subgroups are represented • Increases precision of estimates compared to simple random sampling • Enables subgroup analysis to identify variation sources within specific strata • Ensures representation of minority groups that might be missed in random sampling • Improves efficiency by requiring smaller sample sizes to achieve the same level of precision
How Stratified Sampling Works
Step 1: Define the Population Identify the entire population you want to study.
Step 2: Identify Stratification Variables Determine which characteristics are relevant for dividing the population (e.g., shift, machine, operator, location, time period).
Step 3: Divide into Strata Separate the population into mutually exclusive and collectively exhaustive subgroups.
Step 4: Determine Sample Size Calculate the total sample size needed and allocate samples to each stratum.
Step 5: Select Samples Use random sampling within each stratum to select individual units.
Types of Allocation in Stratified Sampling
• Proportional Allocation: Sample size from each stratum is proportional to the stratum's size in the population • Equal Allocation: Same number of samples taken from each stratum • Optimal Allocation: Sample size based on stratum variability and size
When to Use Stratified Sampling
Use stratified sampling when: • The population has distinct subgroups • You need to compare results across different groups • Certain subgroups are small but important • You suspect different strata have different characteristics • You want to reduce overall sampling variance
Example in Six Sigma Context
A manufacturing plant produces widgets on three shifts. To analyze defect rates, you would: 1. Stratify by shift (Morning, Afternoon, Night) 2. If shifts produce 50%, 30%, and 20% of output respectively 3. From a sample of 100, take 50 from morning, 30 from afternoon, and 20 from night shift
Exam Tips: Answering Questions on Stratified Sampling
Key Recognition Points: • Look for scenarios mentioning subgroups, categories, or distinct populations • Questions often describe situations where certain groups might be underrepresented • Watch for terms like "heterogeneous population" or "distinct characteristics" Common Question Types:
1. Definition questions: Know that strata must be mutually exclusive and collectively exhaustive
2. Application questions: Identify when stratified sampling is the best choice over simple random or systematic sampling
3. Calculation questions: Practice proportional allocation calculations
Remember These Key Points: • Stratified sampling is used when the population is heterogeneous but can be divided into homogeneous subgroups • Each element belongs to only ONE stratum • This method typically provides more precise estimates than simple random sampling • The stratification variable should be related to the variable being measured
Common Exam Traps to Avoid: • Do not confuse stratified sampling with cluster sampling (in cluster sampling, you sample entire groups) • Remember that stratification occurs BEFORE sampling, not after • Strata are based on known characteristics, not random divisions
Quick Memory Aid: Think of stratified sampling as "divide then sample" - you organize first, then randomly select from each organized group.