This document discusses random sampling and its advantages. Random sampling is defined as selecting a sample from a population in a way that every possible sample has an equal probability of being chosen. It avoids potential bias and provides a representative sample of the population. The key steps to conducting a random sample are: 1) defining the population, 2) choosing a sample size, 3) listing the population, 4) assigning numbers to each unit, and 5) randomly selecting the sample. An example of randomly sampling students at a school is provided to illustrate the process.
In the Pharmaceutical, We can get accurate result of the whole population or Whole Batch only and only if Our Sampling Method is perfect and Accurate.
Sampling is also one of the IMP technique for the Statistical calculations.
SAMPLING ; SAMPLING TECHNIQUES – RANDOM SAMPLING (SIMPLE RANDOM SAMPLING)Navya Jayakumar
SAMPLING ; SAMPLING TECHNIQUES – RANDOM SAMPLING
(SIMPLE RANDOM SAMPLING)
Sampling means the process of selecting a part of the population
A population is a group people that is studied in a research. These are the members of a town, a city, or a country.
It is difficult for a researcher to study the whole population due to limited resources
E.G.. Time, cost and energy
Hence the researcher selects a part of the population for his study, rather than selecting the whole population. This process is known as sampling
Also known as Random Sampling
A type of sampling where each member of the population has a known probability of being selected in the sample
When a population is highly homogeneous, its each member has a known chance of being selected in the sample
The extend of homogeneity of a population usually depends upon the nature of the research. E.g.: who are the target respondents of the research
In the Pharmaceutical, We can get accurate result of the whole population or Whole Batch only and only if Our Sampling Method is perfect and Accurate.
Sampling is also one of the IMP technique for the Statistical calculations.
SAMPLING ; SAMPLING TECHNIQUES – RANDOM SAMPLING (SIMPLE RANDOM SAMPLING)Navya Jayakumar
SAMPLING ; SAMPLING TECHNIQUES – RANDOM SAMPLING
(SIMPLE RANDOM SAMPLING)
Sampling means the process of selecting a part of the population
A population is a group people that is studied in a research. These are the members of a town, a city, or a country.
It is difficult for a researcher to study the whole population due to limited resources
E.G.. Time, cost and energy
Hence the researcher selects a part of the population for his study, rather than selecting the whole population. This process is known as sampling
Also known as Random Sampling
A type of sampling where each member of the population has a known probability of being selected in the sample
When a population is highly homogeneous, its each member has a known chance of being selected in the sample
The extend of homogeneity of a population usually depends upon the nature of the research. E.g.: who are the target respondents of the research
This document is quoted from Academic Writing Skill, IFL, Cambodia. It's for students in year three not only at IFL but also other universities in Cambodia.
Sampling is the technique of selecting a representative part of a population for the purpose of determining the characteristics of the whole population. There are two types of sampling analysis: Simple Random Sampling and Stratified Random Sampling. Sampling is useful in assigning values and predicting outcomes for an entire population, based on a smaller subset or sample of the population.
This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection.
This document is quoted from Academic Writing Skill, IFL, Cambodia. It's for students in year three not only at IFL but also other universities in Cambodia.
Sampling is the technique of selecting a representative part of a population for the purpose of determining the characteristics of the whole population. There are two types of sampling analysis: Simple Random Sampling and Stratified Random Sampling. Sampling is useful in assigning values and predicting outcomes for an entire population, based on a smaller subset or sample of the population.
This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection.
This Presentation Will lead you towards a deep and neat study of the research sample and survey. It will be based on the main concepts of sampling types of sampling, types of surveys.
Types of Sampling : Probability and Non-probability
Probability sampling methods:
Simple random sampling
Cluster sampling
Systematic Sampling
Stratified Random sampling
2. Non-Probability:
Convenience sampling
Consecutive sampling
Quota sampling
Judgmental or Purposive sampling
Snowball sampling.
2. 2
What is Random Sampling?
“A method of selecting a sample from a statistical
population in such a way that every possible
sample that could be selected has a predetermined
probability of being selected.”
3. 3
What is Random Sampling?
• Every item has an equal chance of being
selected into the sample
• Avoids potential bias
• Will therefore be a representation of the
population
– A population being a complete set of items (e.g.
people) that share at least one property in common
that can then be statistically analyzed
4. 4
Advantages
• The sample is highly representative of the
population being studied
– Assuming that there is limited missing data
• Since units selected for the sample are chosen
using random sampling, it allows us to make
generalizations (statistical inferences) from the
sample to the population
5. 5
How do you Conduct a Random
Sample?
1. Define the population
2. Choose your sample size
3. List the population
4. Assign numbers to the units
5. Select your sample
6. 6
Example
You are going into a school to analyze the progress
of the students academically and so you want to
take a random sample. You don’t have the time to
test all the students so you want a ‘representative
sample.’
7. 7
1. Define the Population
• Your population in this case are the students
– Which grades?
• You then decide to test grades 2, 4 and 6
8. 8
2. Choose Your Sample Size
• Finding the right sample size is critical
• It needs to be large enough to balance out
outliers and be representative of the population
• But also not too large or else it will take too long
and too many resources
• You decide to test 20 children per grade
9. 9
3. List the Population
• In this case this may already be done for you as
the students are listed in a register
• But if it were not all students from the grades
being tested the students would need to be put
into a list
10. 10
4. Assign Numbers to the Units
• Once the list is compiled all the units, in this
case students, can be assigned numbers
• So in a class of 40 students, the students would
be listed from 1 to 40 alphabetically
11. 11
5. Select Your Sample
• If your sample size is 20 and you have 40
students in a class then mathematically you
would have to test every second student to end
up with 20
• And so in your list of 40 numbered students you
would test every second child
– Child 2, 4, 6, 8 and so on
