A sample is a subset of a population that is used to gain information about the whole population. A population is the complete set of individuals or objects under consideration. Sampling involves selecting a sample in order to make inferences about the population. The main reasons for sampling instead of a complete census are that it is more economical, timely, and sometimes populations are too large or inaccessible. However, samples can contain errors and biases that mean results may not accurately represent the true population.

1. What is a sample?
 A sample is a finite part of a statistical population
whose properties are studied to gain information
about the whole population (e.g. people) set of
respondents selected from a larger pop.
 A population is a group of individuals persons,
objects, or items from which samples are taken for
measurement (e.g. a population of professors, or
students or books).

2. What is sampling
 Sampling is the act, process, or technique of
selecting a suitable sample, or a representative
part of a population for the purpose of
determining parameters or characteristics of the
whole population.

3. What is the purpose of sampling
 To draw conclusions about populations.... to
determine characteristics.....
 We obtain a sample rather than a complete
enumeration (a census ) of the population for
many reasons…..but we should prepare ourselves
to cope with the dangers of using samples.
 There are various kinds of sampling procedures.
Some are better than others but all may yield
samples that are inaccurate and unreliable.

4. Reasons for selecting sample
1. Economy: financial constraints allow you to select a
smaller sample size.
2. Timeliness: allowed time period in which the research
has to be completed
3. The large size of many populations: you cannot sample
the whole population, (only in census one goes door to
door for information)
4. Inaccessibility of some of the pop: some population
areas are hard to reach;
5. Destructiveness of the observation: quality control
6. Accuracy: smaller size of sample would have more
accuracy than a larger and unmanageable sample.

5. Bias and Error in sampling
 Sampling error: is the diff. b/w Sample n pop that are due
solely to a particular units that happen to have been selected
 Two basic causes of Error:
 1. Chance occurs because of bad luck, unusual units in a pop
do exist and there is always a possibility that an abnormally
large number of them will be chosen. For e.g. 50 households
were selected and information was collected about the
number of household members, now normally there are 5 to
6 members but in this sample there were two households that
had 15 to 22 members, this would affect the overall average.

6. Condt.
 2 . Bias sampling bias is a tendency to favour the of unit to have
particular characteristics. It results due to poor sampling plan.
For e.g. a mail questionnaire was sent to 100 randomly selected
graduate students to find out the level of stress they are going
through. Only 52 responded and showed that students were not under
stress whereas other 48 students who did not responded because they
were busy in preparation for the up coming exam. Here another impt.
Issue is that a lot of money was spent on this study now it all wasted.
Clearly it shows NON-RESPONSE
 Non sampling error: solely from the manner in which
the observations are made. E.g. measurement due
malfunctioning of instrument or poor procedure.. For
e.g human weights.. Measuring BP, etc.

7. Bias and Error in sampling
 The interviewer’s effect:
 Manner in which question is formulated
 Individuals tend to provide false answers to a particular question for
example age
 The respondent’s effect:
 To impress the interviewer, ask men and women about the harvest of the last
season you will get different reply, men always tend to lie, so it is most
difficult to prevent. “psychological factors” induce incorrect reposses..
 Knowing the study purpose: why is the study is being conducted
may create incorrect answers for e.g study on find income of household..
 What is your income , if govt. agency is asking you will get different
answer …. May be to dodge Income Tax..
 Induced bias:
 Personal prejudices.. Designer or data collector would ask questions as
they want to have the answer.

8. Sampling methods
 Can be classified as either:
 1. Probability
 2. Non-probability
Probability: each member of the population has a
probability of being selected..e.g. random, systematic,
stratified sampling
Non-Probability: members are selected from the pop. In
some non-random manner..e.g. convenience, judgment,
quota sampling.

9. Types of Samples
 There are three primary kinds of samples:
 the convenience,
 the judgment sample, and
 the random sample.
They differ in the manner in which the elementary units
are chosen

10. Types of Samples contd
 The convenient sample
 A convenience sample results when the more convenient
elementary units are chosen from a population for
observation.
 The judgment sample
 A judgment sample is obtained according to the discretion
of someone who is familiar with the relevant characteristics
of the population.
 The random sample
 This may be the most important type of sample. A random
sample allows a known probability that each elementary
unit will be chosen. For this reason, it is sometimes
referred to as a probability sample.

11. TYPES OF RANDOM SAMPLES
 A simple random sample: Each unit has equal chance of
being selected
 A systematic random sample: Selecting one unit on a
random basis and choosing additional units at evenly spaced
intervals..e.g 100 students…20 to be selected
 A stratified sample: simple random sample from each
stratum..e.g.SES , education, income etc
 A cluster sample: Clusters from the population on the basis
of simple random selection..
e.g.State..Schools..teachers…opinions