Statistical sample (characteristics, types and example)

We can define the statistic sample as the procedure related to the selection of data and the individual observation of a certain population; It helps us to make statistical inferences about a total representation of the data in an appropriate way.

A statistical sampling it is part of a population investigation. For example, if you want to know the salary of part of an average population group, it is not necessary to survey to all the inhabitants of this, so directing the investigation to a small number of people is enough.

In addition, it is impractical not to say that it is impossible to track each member of a population, adding the lack of time and cost, it is difficult to do a total collection of data.

Characteristics that comprise the statistical sample.

When you want to conduct an efficient investigation, the statistical sample quality It's fundamental. It is useless to make the most complex statistical metrics with the best models, if the sample the statistical sample is not representative.

At the time of obtain a representative sample, some aspects that the researcher must know in advance must be taken into account. Within these aspects there are certain characteristics belonging to a representative sample. These characteristics are:

• Selections are made in a representative manner.
• It allows to measure the reliability that exists in the estimate obtained.
• Building on the knowledge gained, you make sure to produce as much information as possible at the lowest cost.
• It is important that it be representative in relation to all the existing data in the set that have similar characteristics.
• It is responsible for determining the stability of the statistics once the test results are identified as the same without taking into account the increase in the size of the sample.

Advantages of the statistical sample.

There are many advantages for companies that implement this type of procedure, among the different benefits to which you have access are these:

• It is convenient to be able to count a population when the population is extremely large and enumeration is impractical.
• It is ideal for use when the population is homogeneous, either representatively or as a sample.
• At times when the process by which the investigation is destructive.

It is also very advantageous when:

• The economy of money and time performs a statistical operation.
• During the application of the scope of an investigation.
• To improve the training, control and training of personnel within a company.
• It has a greater control of the integrity and quality of the investigation.

Types of statistical samples

When making a classification of the various statistical sample types, you can mention the following:

Probabilistic sample

It is the most used in research, since all elements of the population have the possibility of being part of the sample, such as, for example, the population census carried out in a nation. In turn, the following are derived from this type of sample:

1. Simple random sampling

It is the most basic selection method, where each individual has an identification number and through a random lottery they are chosen for the sample. To be carried out, it is necessary to have a clear number of individuals who will complete the complete sample.

2. Systematic sampling

The population to be studied must be enumerated and the researchers will have to be in charge of making lists with each of the individuals in different groups of 10 in a random way. Afterwards, one of each of each group is selected at random and thus the sample will be formed.

3. Stratified sampling

Here the population is classified into groups and similar characteristics will be shared in them. Then, a selection is made of the individuals in each group in a proportional way.

4. Cluster sampling

It occurs if the population is already classified into groups in a natural way. After this, the individuals are selected from each cluster and thus the sample can be formed.

Non-probability sample

In this sample, the elements are selected through various procedures that do not provide the individuals in the population with the same opportunities to be chosen for the sample. This type also includes the following derivatives:

1. Quota sampling

Through it, the researchers carry out the sample by taking into account various characteristics and in this way to be able to achieve in the sample a distribution of characteristics the same as in the population.

2. Convenience sampling

Here the researcher must select the individuals in his sample solely by proximity. Mostly this sample is not recognized by the researcher as part of the representation of the entire population, however, it allows data and information quickly.

3. Snowball sampling

It is used when the researcher needs an individual from his sample to help identify another that has the same characteristics and in turn these to others in order to form the desired sample.

4. Discretionary sample

It refers to the choice of individuals made by the researcher of his sample in relation to certain knowledge of the population.

Example of a statistical sample

For a better understanding of the sample definition, the following example will be shown below:

To study a certain population that includes one million inhabitants of a city where there are titles of degree, the situation of 1000 people who are chosen randomly from the different areas of the city. Through the sample of those 1000 people, the average will be estimated and it is based on this the final conclusion can be reached.

In all statistical research, a collection of information about a certain population is needed. For this, the taking of a statistical sample is used as a strategy, where it is sought to focus attention on a selected group.

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«Some ideas taken from ECONOMIPEDIA.»