Sampling |

Sampling is a statistical procedure of selecting some representative part from a existing population or study area. Specifically draw a sample from study population using some statistical method. For example-

if we want to calculate the average age of Bangladeshi people than we can not deal with the whole population. In that time we must have to deal with some representative part of this population. This representative part is called sample and procedure is called sampling.

**Why need sampling**

—
Sampling makes possible the study of a large population
which contains different characteristics.

—
Sampling is for economy.

—
Sampling is for speed.

—
Sampling is for accuracy.

—
Sampling saves the sources of data from being all
consumed.

Sometimes we can't work with population such as blood test, in that situation sampling is must.

**Types of sampling**

**Probability Sampling**

Probability sampling is based on the concept of random selection where
each population elements have non-zero chance to be occurred as sample.
Sampling techniques can be divided into two categories: probability and
non-probability. Randomization or chance is the core of probability sampling
techniques.

For example, if a researcher is dealing with a population of 100
people, each person in the population would have the odds of 1 out of 100 for
being chosen. This differs from non-probability sampling, in which each member
of the population would not have the same odds of being selected.

Different types
of probability sampling

**Application of probability sampling**

· In
opinion poll, a relatively small number of persons are interviewed and their
opinions on current issues are solicited in order to discover the attitude of
the community as a whole.

· At
border stations, customs officers enforce the laws by checking the effects of
only a small number of travelers crossing the border.

· A
departmental store wises to examine whether it is losing or gaining customers
by drawing a sample from its lists of credit card holders by selecting every
tenth name.

· In
a manufacturing company, a quality control officer take one sample from every
lot and if any sample is damage then he reject that lot.

**Advantages of Probability Sampling**

—
Creates samples that are highly representative of
the population.

—
Sampling bias is tens to zero.

—
Higher level of reliability of research findings.

—
Increased accuracy of sampling error estimation.

—
The possibility to make inferences about the
population.

**Disadvantages of Probability Sampling**

—
Higher complexity compared to non-probability
sampling.

—
More time consuming, especially when creating
larger sample.

—
Usually more expensive than non-probability
sampling.

**Non-Probability sampling**

The process of selecting a sample from a population
without using statistical probability theory is called non-probability sampling.

Example

Lets say that the university has roughly 10000
students. These 10000 students are our population (N). Each of the 10000
students is known as a unit, but its hardly possible to get known and select
every student randomly.

Here we can use Non-Random selection of sample to
produce a result is it called sampling.

**Applications:**

· This
type of sampling can be used when demonstrating that a particular trait exist
in the population.

· It
can also be useful when the researcher has limited budget, time and workforce.

**Advantage:**

·
Select
samples purposively

·
Enable
researchers to reach difficult to identify members of the population.

·
Lower
cost

·
Limited
time.

**Disadvantage:**

Difficult to make valid inference about the entire
population because the sample selected is not representative.

We cannot calculate confidence interval.

**Types of non probability sampling:**

·

- Accidental sampling
- Purposivesampling
- Snowballsampling
- Convenience sampling

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