Sampling- Basics of testing hypothesis - sampling, essence of sampling, types of sampling
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The document discusses sampling methods used in research, emphasizing the importance of selecting an appropriate technique for accurate results. It outlines four primary sampling methods: simple random sampling, stratified random sampling, cluster random sampling, and systematic random sampling, each accompanied by a brief description and example. The text illustrates how these methods ensure representation and reliability in research data collection.
Sampling- Basics of testing hypothesis - sampling, essence of sampling, types of sampling
- 1. Ravinandan A P Assistant Professor Sree Siddaganga College of Pharmacy Tumkur
- 2. Ravinandan A P 2 Sampling, • Sampling, in simple terms, means selecting a group (a sample) from a population from which we will collect data for our research. • Sampling is an important aspect of a research study as the results of the study majorly depend on the sampling technique used. • So, in order to get accurate results or the results that can estimate the population well, the sampling technique should be chosen wisely.
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- 8. Ravinandan A P 8 Sampling Methods 1. Simple random sample 2. Stratified random sample 3. Cluster random sample 4. Systematic random sample
- 9. Ravinandan A P 9 Simple random sample (lottery method): • Every member and set of members has an equal chance of being included in the sample. • Technology, random number generators, or some other sort of chance process is needed to get a simple random sample. • Example—A teachers puts students' names in a hat and chooses without looking to get a sample of students. • Why it's good: Random samples are usually representative since they don't favor certain members.
- 10. Ravinandan A P 10 Stratified random sample: • The population is first split into groups. The overall sample consists of some members from every group. The members from each group are chosen randomly. • Example—A student council surveys 100 students by getting random samples of 25 freshmen, 25 sophomores, 25juniors, and 25 seniors. • Why it's good: A stratified sample guarantees that members from each group will be represented in the sample, so this sampling method is good when we want some members from every group.
- 11. Ravinandan A P 11 Cluster random sample: • The population is first split into groups. The overall sample consists of every member from some of the groups. The groups are selected at random. • Example—An airline wants to survey its customers one day, so it randomly selects 555 flights and surveys every passenger on those flights. • Why it's good: A cluster sample gets every member from some of the groups, so it's good when each group reflects the population as a whole.
- 12. Ravinandan A P 12 Systematic random sample: • Members of the population are put in some order. A starting point is selected at random • Example—A principal takes an alphabetized list of student names and picks a random starting point.
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