What is Random Sampling?

May 4, 2018

Definition of Random Sampling

Sampling is based on probability theory –in its broadest sense, if we can choose respondents randomly and appropriately from the larger population, the results from that random sample will be very close to what we would get by interviewing every member of the population.

Random sampling is a technique in which each sample has an equal probability of being chosen. A sample chosen randomly is meant to be an unbiased representation of the total population.

Random sampling is a part of the sampling technique in which each sample has an equal probability of being chosen. A sample chosen randomly is meant to be an unbiased representation of the total population. If for some reasons, the sample does not represent the population, the variation is called a sampling error.

Overview of Random Sampling

Random sampling is one of the simplest forms of collecting data from the total population. Under random sampling, each member of the subset carries an equal opportunity of being chosen as a part of the sampling process. For example, the total workforce in organizations is 300 and to conduct a survey, a sample group of 30 employees is selected to do the survey. In this case, the population is the total number of employees in the company and the sample group of 30 employees is the sample. Each member of the workforce has an equal opportunity of being chosen because all the employees which were chosen to be part of the survey were selected randomly. But, there is always a possibility that the group or the sample does not represent the population as a whole, in that case, any random variation is termed as a sampling error.

Figure: Random Sampling

Random sampling means that members of a ‘population’ have equal chances of being selected.

To carry out this type of sampling, you will need to use a table of random numbers. Random numbers can also be generated using a calculator or computer. These can then be listed.

For example, if these random numbers are generated by a calculator:

017, 029, 300, 914, 037, 849, 111, 559, 333, 400, 598, 255.

Use these to make up the list of numbers to select a sample:

0 1 7 0 2 9 3 0 0 9 1 4 0 3 7 8 4 9 1 1 1 5 5 9 3 3 3 4 0 0 5 9 8 2 5 5

The advantages and disadvantages of simple random sampling are explained below. Many of these are similar to other types of probability sampling technique, but with some exceptions. Whilst simple random sampling is one of the ‘gold standards’ of sampling techniques, it presents many challenges for students conducting dissertation research at the undergraduate and master’s level.

• The aim of the simple random sample is to reduce the potential for human bias in the selection of cases to be included in the sample. As a result, the simple random sample provides us with a sample that is highly representative of the population being studied, assuming that there is limited missing data.
• Since the units selected for inclusion in the sample are chosen using probabilistic methods, simple random sampling allows us to make generalizations (i.e., statistical inferences) from the sample to the population. This is a major advantage because such generalizations are more likely to be considered to have external validity.

A simple random sample can only be carried out if the list of the population is available and complete. Attaining a complete list of the population can be difficult for a number of reasons:

• Even if a list is readily available, it may be challenging to gain access to that list. The list may be protected by privacy policies or require a lengthy process to attain permissions.
• There may be no single list detailing the population you are interested in. As a result, it may be difficult and time consuming to bring together numerous sub-lists to create a final list from which you want to select your sample. As an undergraduate and masters level dissertation student, you may simply not have sufficient time to do this.
• Many lists will not be in the public domain and their purchase may be expensive; at least in terms of the research funds of a typical undergraduate or master’s level dissertation student.
• In terms of human populations (as opposed to other types of populations; see the article: Sampling: The basics), some of these populations will be expensive and time consuming to contact, even where a list is available. Assuming that your list has all the contact details of potential participants in the first instance, managing the different ways (e.g., postal, telephone, email) that may be required to contact your sample may be challenging, not forgetting the fact that your sample may also be geographical scattered.

References

[1] “Definition of ‘Random Sampling”, available online at: https://economictimes.indiatimes.com/definition/random-sampling

[2] “Random sampling”, available online at: http://www.bbc.co.uk/schools/gcsebitesize/maths/statistics/samplinghirev2.shtml

[3] “Simple random sampling”, available online at: http://dissertation.laerd.com/simple-random-sampling.php

[4] “How Random Sampling in Hive Works, And How to Use It”, available online at: https://hadoopsters.net/2018/02/04/how-random-sampling-in-hive-works-and-how-to-use-it/

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