This document discusses key concepts related to sampling, including defining a population and sample. It notes that sampling allows for convenient and economical study of large populations, and can increase the speed, accuracy, and rapport of data collection if done properly. However, sampling also carries risks of bias and requires expertise. The document outlines criteria for good sampling, such as the sample being representative and an appropriate size. It also discusses sampling error and how sample size and population variability can affect statistical inference drawn from samples.

1. Dr Neeta Gupta
Positive Psychologist
Associate Professor
Certified Practitioner of EFT & REBT (London)
& CBT (Scotland)
DAV PG College
Dehradun
Sampling

2. Some Concepts to be understood:
Population: It includes each and
every individual to be studied.
Sample:A representative part of the
population.

3. Sampling: Is the process of selecting a
representative group of individuals from
the population.

4. Importance of Sampling:
It is convenient and time saving.
Large population can also be studied
through sampling.
It is Economical.
It increases the speed of data-
collection,analysis and interpretation.
The results found are more accurate.
Better rapport is established with the
participants.

5. Sampling Disadvantages:
1.Chances of Biases: Due to incorrect
sampling.
2.Needs Expertise: Sampling procedure
needs a lot of knowledge, training and
experience.
3.Too small population: Sometimes the
population is too small to draw a
representative sample.

6. Cretiron of Good Sampling:
1.A Sample must be representative of
the population.
2.It should be of accurate size.
3.It should be free from random
sampling error.
4.It should be free from errors due to
biases.

7. Sampling Errors:

8. A sampling error is the difference
between parameter and statistics.
Parmeter The corresponding measure
which is usually unknown and based upon
the population is known as parameter.
Statistic is the statistical measure based
upon sample.
Statistical Inferences When results
obtained from a limited number of
individuals are applied to a larger number
it is called SI.

9. Sampling error is inversely related to
statistical inferences. The higher the
sampling error the poorer the
statistical inference.
The amount of Sampling error
depends upon the two factors:
1.Variability in the population : If the
nature of the population is not stable
and tends to vary it would affet the
representativeness of the sample.

10. 2. Size of the sample The larger
the sample the smaller would
be the error.
According to Uma Sekaran
sample size larger than 30 and
less than 500 are appropriate
for most research and the
minimum size of sample should
be 30% of the population.

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Links of the picture references
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