Stratified Random Sampling

Stratified Random Sampling

Stratified random sampling is a sampling technique that divides the population into smaller groups, called strata, based on shared characteristics. Then, a random sample is drawn from each stratum, ensuring that the sample proportions of each stratum are proportional to their prevalence in the population.

Process:

1. Stratification: Divide the population into distinct strata based on shared characteristics, such as age groups, genders, socioeconomic status, or geographical regions.
2. Sample Selection: Randomly select a sample of units from each stratum. The number of units selected from each stratum is proportional to its size in the population.
3. Combining Samples: Combine the samples from all strata to form a final sample that represents the entire population.

Advantages:

• Increased Precision: Stratified sampling can provide more precise estimates than simple random sampling, as it reduces sampling variability by ensuring a more representative sample from different groups.
• Enhanced Representativeness: The sample is more likely to reflect the diversity of the population, as it includes a proportional number of units from each stratum.
• Reduced Bias: Stratified sampling can reduce bias, as it minimizes the influence of sampling errors on specific groups.

Disadvantages:

• Stratification Complexity: Stratification can be complex, especially with large populations and many strata.
• Sample Size: The number of units required for each stratum may increase the overall sample size.
• Strata Imbalance: If one stratum has a very low population, it may be difficult to draw a sufficient sample from that group.

Examples:

• Sampling students from different age groups in a school.
• Selecting voters from different political parties in a election poll.
• Collecting data on housing prices in different neighborhoods.

In summary, stratified random sampling is a sampling technique that increases precision, enhances representativeness, and reduces bias by dividing the population into strata and selecting a random sample from each stratum. However, it can be more complex than simple random sampling, and the sample size may increase.