An Overview of Sampling Techniques in Statistics

Abstract, 2017
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Sampling Techniques

Question 1

In statistics, sampling entails the selection of a subset of population from within a chosen statistical population to approximate the characteristics or features of the whole population. Statistical sampling is preferred when studying a population as it is cost effective, allows faster data collection as well as provides the possibility of improving quality and accuracy of data ( ).

Several sampling techniques are used depending on nature of studied population. The different types of sampling techniques include;

1. Simple random sampling

This is the most common type of sampling technique whereby all subsets of a population are given equal probability. Each subset thus has an equal probability of selection. This helps to minimize bias and simplify the process of data analysis.

Moreover, the discrepancy between individual outcomes within the sample population is a good indicator of adjustments in the general population, thus making it relatively easy to guess the accuracy of results (

Advantages and disadvantages of random sampling


- It can be used when dealing with large sample populations
- It is an excellent technique of avoiding bias in a study population


- It is prone to sampling error since the randomness of the population may result in a sample that does not accurately reflect the general makeup of the study population.
- As it covers large sample populations, practical limitations in terms of time available is a limiting factor thus certain parts of the area may not be studied.

2. Systematic sampling

This sampling technique relies on placing the study population according to some random start, ordering scheme and then picking out elements at steady intervals through that ordered list. However, as long as the as long as the starting point is randomized, systematic sampling is a form of probability sampling.

However, if periodicity is present and the period is a multiple or factor of the interval used, the sample is especially likely to be un representative of the overall population, decreasing its accuracy. Another drawback of systematic sampling is that even in scenarios where it is more accurate than SRS, its theoretical properties make it difficult to quantify that accuracy. As described above, systematic sampling is an EPS method, because all elements have the same probability of selection.

Advantages and disadvantages of systematic sampling


- It is more straight-forward and easy to implement than random sampling
- Does not require a grid since the sampling just has to be at even intervals
- The technique increases the chances of covering a broader area of study population.


- It may lead to underrepresentation of a particular pattern whenever periodicity is present and it is factor of interval used.

- The technique is more biased since not all points or members have an equal chance of being chosen.

3. Stratified sampling

Form of sampling that is used when the study population embraces several diverse categories.4 the categories are thus arranged in strata. Each stratum is then selected as independent sub population out of which individual aspects can be randomly picked.

Advantages of stratified sampling

- It is flexible and applicable in many geographical enquiries
- It can be used with systematic or random sampling, and with line, area or point techniques
- If the subsets magnitudes are known, it can generate outcomes that are more representative of the total population
- comparisons and correlations can be made between sub-sets


- It can be a challenge to stratify questionnaire data collection since accurate up to date population data may not be available and it may be hard to identify social background or people’s age effectively
- The proportions of the sub-sets must be identified and accurate if it is to work properly

4. Probability-Proportional-to-Size Sampling

Probability-proportional-to-size (PPS) is sampling technique in which the selection likelihood for each component is set to be relative to its magnitude, up to a maximum of 1.

Example includes businesses surveys, where size of elements varies greatly and supplementary information is often available.


a. This technique improves accuracy for a given sample size by focusing the sample on large components that have the greatest effect on population estimates.

5. Cluster Sampling

Type of sampling in which sample population is selected in groups (clusters) (

Sampling is often clustered by time periods or geography.


a. Cluster sampling generally increases the variability of sample estimates above that of simple random sampling, depending on how the clusters differ between themselves, as compared with the within-cluster variation.

b. Clustering can reduce travel and administrative costs.

6. Quota Sampling

In this type of sampling method, the population is first segmented into equally exclusive subdivisions, just as in the case of stratified sampling ( . After placing the components in their subgroups, judgment is then used to select the units or subjects from each segment based on a specified proportion.

For instance, an interviewer may be told to sample 30 males and 20 females between the age of 35 and 50.


a. In quota sampling the selection of the sample is non-random.


a. samples may be biased because not everyone gets a chance of selection.

7. Accidental Sampling

Accidental sampling also known as opportunity, convenience or grab sampling is a non-probability sampling which encompasses the sample being selected from that section of the population which is close to hand (


a. It is fast and cost effective


a. The researcher cannot scientifically make generalizations about the total population from this sample because it would not be representative enough.

8. Panel Sampling

Is a sampling technique of first picking a group of participants through a random sampling process and then asking that group for the same information again multiple times over a selected period of time (


a. allows estimates of changes in the population.

9. Purposive Sampling

This is a sampling technique in which the components are chosen based on the study objectives. It may also entail studying the whole population of some limited group or a subgroup of a population


a. it does not produce a sample that is representative of a larger population,

Snowball Sampling

This is a technique in which a researcher finds one member of some population of interest, collects all necessary information from him or her then asks the studied person to direct the researcher to another person the researcher might speak to. The process of referral goes on and on until the researcher completed his or her project.


1. very good for cases where members of a special population are difficult to locate

2. excellent method to study a give n pattern of a subgroup


a. It can be difficult to determine how a sample compares to a larger population.

b. snowball sampling often leads the researcher into a realm he/she is unfamiliar with

c. There's an issue of who respondents refer you to - friends refer to friends, less likely to refer to ones they don't like, fear (

Question 2

As defined by, a simulation is the production of a computer model of something. It is an imitation of a process or situation. Historical simulation entails the utilization of historical records of random variables or returns to simulate the probable outcomes. This method holds the assumption that past performance is an indication of future performance.

On the contrary, Monte Carlo simulation depends on modeling the extensiveness of risk factors using a random number creator. It entails the construction of a computer-based model that integrates all the random variables that may affect the performance of a project, including any interdependencies, interrelationships and serial associations between them.

Monte Carlo simulation is mainly applied financial risk management whereas Historical information is mainly used by banks to calculate value at risks (“shortcomings of historical simulation”

Work Cited

“Different Sampling and their advantages and Disadvantages” 20 Mar 2017. Web. 19 May 2017

“Monte Carlo vs. Historical Information” 28 Sept 2011. Web 19 May 2017

“Sampling Techniques ” 11 Apr 2016. Web. 19 May 2017

“Sampling Techniques” 2 Jul 2014 Web. 19 May 2017

shortcomings of historical simulation” 17 Feb 2010. Web. 19 May 2017

“QMSS e Lessons/Type of Sampling” 17 Apr 2015. Web. 19 May 2017

“What is Simulation?” 6 Mar 2013. Web. 19 May 2017“

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An Overview of Sampling Techniques in Statistics
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statistics, sampling, data, statistical sampling, sampling techniques, simulation
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Dan L Gor (Author), 2017, An Overview of Sampling Techniques in Statistics, Munich, GRIN Verlag,


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