Stratified Sampling

Stratified sampling is a sampling technique where the population is divided into homogeneous groups or strata based on certain characteristics. Within each stratum, a simple random sampling method is applied to select a sample. This sampling method provides separate estimates for population means for different segments and also an overall estimate.

Explanation:

1. Separate Estimates for Population Means

In stratified sampling, the population is divided into different strata based on certain characteristics. These characteristics can be demographic variables such as age, gender, income level, or geographic variables such as regions or cities. Each stratum represents a separate segment of the population.

Within each stratum, a simple random sampling method is used to select a sample. This means that individuals are randomly selected from each stratum. By doing so, the sample obtained from each stratum is representative of that particular segment of the population.

Since each stratum is selected independently, separate estimates for population means can be calculated for each segment. This allows researchers to understand the characteristics and trends within different segments of the population.

2. Overall Estimate

After obtaining separate estimates for each segment, an overall estimate can be calculated by combining the estimates from each stratum. This is done by taking into account the relative size or weight of each stratum in the population.

The overall estimate provides a summary of the population means across all segments. It takes into consideration the variability within each stratum as well as the differences between strata.

Advantages of Stratified Sampling

- Provides separate estimates for population means for different segments: Stratified sampling allows researchers to obtain specific estimates for different segments of the population, which can be useful for targeted analysis and decision-making.

- Increases precision and reduces sampling error: By dividing the population into homogeneous strata, stratified sampling ensures that each stratum is well-represented in the sample. This reduces the potential for bias and increases the precision of the estimates.

- Allows for comparison between segments: Since separate estimates are obtained for each segment, researchers can compare and analyze the characteristics and trends within different segments of the population.

- Provides an overall estimate: The overall estimate obtained through stratified sampling provides a comprehensive summary of the population means, taking into account the variability within and between strata.

Overall, stratified sampling is a powerful sampling technique that provides separate estimates for population means for different segments and also an overall estimate. It allows researchers to gain insights into the characteristics and trends within different segments of the population while providing a comprehensive summary of the population means.