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Assistant Professor
Sree Siddaganga College of Pharmacy
Tumkur
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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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Sampling Methods
1. Simple random sample
2. Stratified random sample
3. Cluster random sample
4. Systematic random sample
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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.
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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.
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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.
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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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Sampling- Basics of testing hypothesis - sampling, essence of sampling, types of sampling

  • 1.
    Ravinandan A P AssistantProfessor Sree Siddaganga College of Pharmacy Tumkur
  • 2.
    Ravinandan A P2 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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    Ravinandan A P8 Sampling Methods 1. Simple random sample 2. Stratified random sample 3. Cluster random sample 4. Systematic random sample
  • 9.
    Ravinandan A P9 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 P10 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 P11 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 P12 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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