A sample design is a plan for selecting a sample from a population that considers factors like the type of population, sampling unit, sample size, parameters of interest, and budget. There are two types of sampling designs: probability sampling, where every item has an equal chance of selection, and non-probability sampling, which does not assign selection probabilities. Probability sampling methods include systematic, stratified, multi-stage, and cluster sampling.

1. SAMPLING
DESIGN

2. Sample Design
A sample design is a definite plan for obtaining a sample
from a given population.
It refers to the technique or the procedure the researcher would
adopt in selecting items for the sample.
Steps in Sample Design
 Type of universe
 Sampling unit
 Source list
 Size of sample
 Parameters of interest
 Budgetary constraint
 Sampling procedure

3. Criteria of selecting a Sampling Procedure
There are two types of costs are involved in a sampling analysis:-
 Cost of collecting the data
 Cost of an incorrect inference
Two causes of incorrect inferences:-
 Systematic bias
 Sampling error
Characteristics of a Good Sample Design
 Small sampling errors
 Viable in the context of funds available
 Systematic bias can be controlled in a better way

4. Types of Sample Designs
 Non-probability sampling
 Probability sampling
Non-probability Sampling
Non-probability sampling is that sampling is
procedure which does not afford any basis for estimating the probability
that each item in the population has of being included in the sample.
Probability Sampling
Probability sampling is also known as ‘random sampling’
or chance sampling’. Under this sampling design, every item of the
universe has an equal chance of inclusion in the sample.

5. Probability Sampling
 Systematic sampling
 Stratified sampling
 Multi-stage sampling
 Cluster sampling
 Area sampling
 Sequential sampling
 Sampling with probability proportional to size