This document discusses different concepts related to sampling from a population. It defines key terms like population, sample, parameter, and statistic. It also describes different sampling techniques like purposive sampling, random sampling, stratified sampling, and systematic sampling. Purposive sampling targets a specific group that is difficult to locate. Random sampling gives each element an equal chance of selection. Stratified sampling divides the population into known groups and then randomly samples within each group. Systematic sampling selects every kth element from an ordered list.

1. SAMPLING
By:-
Anadi Vats
100101030
CSE-A

2. POPULATION,SAMPLE AND SAMPLING
Group of individual under study is called population. It may be finite or
infinite.
A part selected from the population is called sample
The process of selecting a sample is called sampling.
If there are C(N,n) types of sample then size of the sample is n
that can be picked up from a population of size N

3. PARAMETERS AND STATISTICS
The constants of population are
Mean (µ)
Standard deviation (σ)
both of them are called PARAMETERS
Mean and standard deviation of a sample are known as STATISTICS.

4. TYPES OF SAMPLING
PURPOSIVE SAMPLING
RANDOM SAMPLING
STRATIFIED SAMPLING
SYSTEMATIC SAMPLING
PURPOSIVE SAMPLING
Purposive sampling targets a particular group of people. When the desired
population for the study is rare or very difficult to locate and recruit for a
study, purposive sampling may be the only option.
Example: if u want to study on the population who are suffering from
blood cancer. It is quiet difficult population to find.

5. RANDOM SAMPLING
Each element in the population have equal probability of selection and
each combination of an element have equal probability of selection
Random numbers to select from an ordered list
Example: names drawn from a hat

6. STRATIFIED SAMPLING
Divide population into groups that differ in important ways
Basis for grouping must be known before sampling
Select random sample from within each group.
Example: let us consider we all students are population and
divide these into two groups one who wearing specs
and other who don’t.
It reduces error than the random sampling.

7. SYSTEMATIC SAMPLING
It is a statistical method involving the selection of elements from an
ordered sampling frame.
The most common form of is an equal-probability method, in which every
kth element in the frame is selected, where k, the sampling interval
k=N/n
Where n is the sample size and the N is the population
size.
Example: Suppose a supermarket wants to study buying habits of their
customers, then using systematic sampling they can choose
every10th or 15th customer entering the supermarket and
conduct the study on this sample.

8. STANDARD ERROR
Standard error is the standard deviation of sampling distribution.
It is used for accessing the difference between the expected value and
observed value.

9. SAMPLING DISTRIBUTION

10. SAMPLE REPLACEMENT