Sampling is used to learn about a population by studying a subset of it. It allows researchers to gather information in a time and cost-effective manner. There are two main types of sampling: probability sampling, where every item has an equal chance of being selected, and non-probability sampling, which has no basis for estimating selection probabilities. Some common sampling designs include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and quota sampling. Good sample design ensures representativeness, adequacy, independence, and homogeneity while accounting for resources and study goals.

1. SAMPLING

2. SamplingSampling is simply the process of learning about the population on the basis of a sample drawn from it.Sampling is a tool which helps to know the characteristics of the universe or population by examining only a small part of it.

3. TerminologyPopulation - Population is the group which is used to generalize the study.Sample - Sample is the group of people that is to be selected for study.Census- A census is the process of obtaining information about every member of a populationSampling Design- It is a technique or procedure for selecting item for the sample.

4. Sampling frame- The listing of the accessible population from which the sample is drawn is called sampling frame.Response- Response is a specific measurement value that a sampling unit supplies.Population Parameter – When mean or average is calculated on entire population, this is not referred as statistics, it is a population parameter.

5. Why Sampling???Time constraintsCost constraintsEasy and convenient ProcessingReuse of populationLesser resources requiredHelpful in studying the hypothetical data.

6. SAMPLING MODEL

7. Characteristic of samplingRepresentativenessAdequacyIndependenceHomogeneity

8. Steps in sample designType of UniverseSampling UnitSource ListSize of SampleParameters of InterestBudgetary ConstraintsSampling Procedure

9. Types of Sampling DesignNON PROBABILITY SAMPLINGPROBABILITY SAMPLINGJUDGEMENT SAMPLINGSIMPLE RANDOM SAMPLINGSYSTEMATIC SAMPLINGQUOTA SAMPLINGCLUSTER SAMPLINGCONVENIENCE SAMPLINGSTRATIFIED SAMPLING

10. NON PROBABILIITY SAMPLINGIt is a sampling procedure which does not afford any basis for estimating the probability that each item in the population has of being included in the sample. Also known as deliberate sampling, purposive sampling.

11. PROBABILITY SAMPLINGProbability sampling is a sampling procedure where every item in the universe has an equal chances of inclusion in the sample. Also known as ‘random sampling’ or ‘chance sampling’.

12. CHARATERSTICS OF GOOD SAMPLE DESIGNMust result in a truly representative sampleMust result in small sampling errorsMust be viable in context of funds available for the research study.Must be such so that systematic bias can be controlled in a better way.

13. Techniques of selecting arandom sampleSize of sample - Size of universe - Resources available - Degree of accuracy - Homogeneity - Nature of study - Method of sampling adopted - Nature of respondents

14. Merits of samplingLess time consumingLess costMore reliable resultsMore detailed informationPractical methodTo judge the accuracy of the information obtained on a census basis.

15. Limitations of samplingUnplanned sampling is often misleadingRequires expertsAt times complicatedNot appropriate when every unit in the domain has to be studied.

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