🧩 Define Stratified Sampling.

🧩 Define Stratified Sampling.

Answer: In Stratified Random Sampling, the population is divided into homogeneous subgroups (strata) based on certain characteristics (like region, gender, income group), and random samples are drawn independently from each stratum.

Types of Allocation:

1. Equal Allocation​

2. Proportional Allocation

3. Optimum (Neyman) Allocation

   
   

Advantages:

• More precise than SRS (reduces sampling error).

• Ensures representation of all key subgroups.

• Allows separate estimates within strata.

  
  

Disadvantages:

• Requires prior information for stratification.

• Improper stratification can reduce efficiency.

Follow-up:

👉 What are the advantages of stratification?

→ Increases precision, ensures representativeness, allows subgroup analysis.

Practical: 👉 How would you decide the number of strata and their sizes?

→ Based on variability within population — more strata for more heterogeneity. Each stratum should be internally homogeneous and externally heterogeneous.

ISS Context Example:In Labour Force Survey, India is divided into strata based on urban/rural and state regions to ensure national representativeness.