Sampling errors occur when using a sample to make inferences about a population. There are two main types of sampling errors - random sampling error and bias sampling error. Random sampling error is caused by chance fluctuations in who is selected in the sample and usually balances out, while bias sampling error results from flaws in the sampling design or implementation and does not balance out. Some factors that influence the size of sampling errors are the sample size, with larger samples having smaller errors, and the heterogeneity of the population. Non-sampling errors also exist, such as errors in defining the population, sampling methodology, non-responses, and measurement errors.

1. SAMPLING ERRORS
Presented By : Angela Mary George
S-2,MBA (Evening)
IMK,Karyavattom

2. OUTLINE OF PRESENTATION
 SAMPLING TERMINOLOGY
 NEED OF SAMPLING
SAMPLING PROCESS
SAMPLING TYPES & TECHNIQUES
SAMPLING ERRORS

3. SAMPLING TERMINOLOGY
Sample: Sample is a subset of population,
selected in such a way that it is
representative of the larger population.
Population: any complete group of entities
that share some common set of
characteristics.
Population element: an individual member
of a population.
Sampling frame: a list of Population from
which a sample is selected.
– Example: student email list, membership
list.

4. Sampling unit: is the unit of
selection.
Sample size: the number of units or
subjects sampled for inclusion in the
study is called sample size.
Sampling technique: Method of
selecting sampling units from
sampling frame.
SAMPLING TERMINOLOGY

5. SAMPLING PROCESS
Sampling is the process
of selecting observations
(a sample) from a
population to provide an
adequate description and
inferences of the
population.

6. NEED OF SAMPLING
Sampling- a valid alternative to a census when :
A survey of the entire population is impracticable
Budget constraints restrict data collection
Time constraints restrict data collection
Results from data collection are needed quickly

7. TYPES OF SAMPLING
PROBABILITY SAMPLING
(RANDOM SAMPLING)
Simple Random Sampling
Systematic Random
Sampling
Stratified Random
Sampling
Cluster Random Sampling
Multistage Random
Sampling
NON-PROBABILITY SAMPLING
(NON-RANDOM SAMPLING)
Quota Sampling
Purposive/ Judgmental
Sampling
Snowball/ Network
Sampling
Convenience/ Grab
Sampling

8. SAMPLE ERRORS
Error caused by the act of
taking a sample
sample results to be different
from the results of real
population (census)
Expressed as “standard error”
We have no control over
Sample error depends upon:
• Size of the sample (larger
size  lesser error)
• Distribution of character
of interest in population
NON - SAMPLE ERRORS
(SYSTEMATIC ERRORS)
Results from some imperfect
aspect of the research design
such as mistakes in sample
selection, sampling frame
error, or non–responses.
Errors not due to chance
fluctuations, but due to
errors resulting from the
researcher
ERRORS ASSOCIATED WITH SAMPLING

9. SAMPLING ERRORS
SAMPLING ERROR: is incurred when the statistical
characteristics of a population are estimated from a subset,
or sample, of that population.
For example, if one measures the height of a thousand
individuals from a country of one million, the average
height of the thousand is typically not the same as the
average height of all one million people in the country.

10. Random Sampling Error:- Random error is a
pattern of errors that tend to cancel one another out
so that the overall result still accurately reflects the
true value. Every sample design will generate a
certain amount of random error
Bias Sampling Error:- Bias, on the other hand, is
more serious because the pattern of errors is loaded
in one direction or another and therefore do not
balance each other out, producing a true distortion.
TYPES OF SAMPLING ERRORS

11. 5 COMMON ERRORS IN THE RESEARCH PROCESS
• 1. POPULATION SPECIFICATION
Example: Packaged goods manufacturers often conduct
surveys of housewives, because they are easier to
contact, and it is assumed they decide what is to be
purchased and also do the actual purchasing. In this
situation there often is population specification error.
The husband may purchase a significant share of the
packaged goods, and have significant direct and indirect
influence over what is bought. For this reason, excluding
husbands from samples may yield results targeted to the
wrong audience.

12. • 2. SAMPLING
Example: Suppose that we collected a random sample of
500 people from the general Indian adult population to
gauge their entertainment preferences. Then, upon
analysis, found it to be composed of 70% females. This
sample would not be representative of the general adult
population and would influence the data. The
entertainment preferences of females would hold more
weight, preventing accurate extrapolation to the Indian
general adult population. Sampling error is affected by
the homogeneity of the population being studied and
sampled from and by the size of the sample
5 COMMON ERRORS IN THE RESEARCH PROCESS

13. • 3. SELECTION
Example: Interviewers conducting a mall intercept
study have a natural tendency to select those
respondents who are the most accessible and
agreeable whenever there is latitude to do so. Such
samples often comprise friends and associates who
bear some degree of resemblance in characteristics
to those of the desired population.
5 COMMON ERRORS IN THE RESEARCH PROCESS

14. • 4. NON-RESPONSIVE
Example: In telephone surveys, some respondents are
inaccessible because they are not at home for the initial call
or call-backs. Others have moved or are away from home for
the period of the survey. Not-at-home respondents are
typically younger with no small children, and have a much
higher proportion of working wives than households with
someone at home. People who have moved or are away for
the survey period have a higher geographic mobility than the
average of the population. Thus, most surveys can anticipate
errors from non-contact of respondents. Online surveys seek
to avoid this error through e-mail distribution, thus
eliminating not-at-home respondents.
5 COMMON ERRORS IN THE RESEARCH PROCESS

15. • 5. MEASUREMENT
Example: A retail store would like to assess customer
feedback from at-the-counter purchases. The survey
is developed but fails to target those who purchase
in the store. Instead, results are skewed by
customers who bought items online.
•
5 COMMON ERRORS IN THE RESEARCH PROCESS