The **branches of statistics** They are disciplines in which statistics rely to analyze data from different perspectives, such as descriptive statistics, inferential statistics or mathematics.

Remember that the statistics It is a branch of mathematics, which corresponds to the collection, analysis, interpretation, presentation and organization of data (set of values of qualitative or quantitative variables).

This discipline seeks to explain the relationships and dependencies of a phenomenon (physical or natural). Statistics is a transversal science, that is, applicable to a variety of disciplines, ranging from physics to social sciences, health sciences or quality control.

In addition, it is of great value in business or government activities, where the study of the data obtained makes it easier to make decisions or make generalizations.

**Main branches of statistics**

Statistics is divided into two large areas: descriptive statistics and inferential statistics, which comprise applied statistics.

In addition to these two areas, there is mathematical statistics, which comprises the theoretical bases of statistics.

**1. Descriptive statistics**** **

The **Descriptive statistics **It is the branch of statistics that describes or summarizes in a quantitative (measurable) way characteristics of a collection of information.

That is, descriptive statistics is responsible for summarizing a statistical sample (data set obtained from a population) instead of learning about the population that the sample represents.

Some of the measures commonly used in descriptive statistics to describe a data set are measures of central tendency and measures of variability or dispersion.

As for the measures of central tendency, measures such as the mean, median and mode are used. While in variability measures, variance, kurtosis, etc. are used.

Descriptive statistics is usually the first part to be performed in a statistical analysis. The results of these studies are often accompanied by graphs, and they form the basis of almost any quantitative (measurable) data analysis.

An example of descriptive statistics might be considering a number to summarize how well a hitter is performing in baseball.

Thus, the number is obtained by the number of hits a batter has given, divided by the number of times he has been at bat. However, this study will not give more specific information, such as which of those hits were home runs.

Other examples of descriptive statistics studies can be: the average age of citizens living in a certain geographic area, the average length of all books referring to a specific topic, the variation regarding the time that visitors spend browsing a Internet page.

**2. Inferential statistics**

The **inferential statistics** it differs from descriptive statistics mainly by the use of inference and induction.

That is, this branch of statistics seeks to deduce properties of a studied population, that is, it not only collects and summarizes the data, but also seeks to explain certain properties or characteristics from the data obtained.

In this sense, inferential statistics implies obtaining the correct conclusions from a statistical analysis carried out using descriptive statistics.

For this reason, many of the experiments in the social sciences involve a small population group, and through inferences and generalizations it is possible to determine how the population in general behaves.

The conclusions obtained through inferential statistics are subject to randomness (absence of patterns or regularities) but by applying the appropriate methods, relevant results are obtained.

Thus, both the **Descriptive statistics **as the **inferential statistics **They go hand in hand.

Inferential statistics is divided into:

**parametric statistics**

Includes statistical procedures based on the distribution of real data, which are determined by a finite number of parameters (a number that summarizes the amount of data derived from a statistical variable).

To apply parametric procedures, for the most part, it is required to previously know the form of distribution for the forms resulting from the population studied.

Therefore, if the distribution of the data obtained is completely unknown, a non-parametric procedure should be used.

**nonparametric statistics**

This branch of inferential statistics includes the procedures applied in statistical tests and models, in which their distribution does not conform to the so-called parametric criteria. As the data studied is what defines its distribution, it cannot be previously defined.

Non-parametric statistics is the procedure that should be chosen when it is unknown whether the data conform to a known distribution, so that it can be a previous step to the parametric procedure.

Likewise, in a nonparametric test, the chances of error are decreased by using adequate sample sizes.

**3. Mathematical statistics**

Mention has also been made of the existence of the** mathematical statistics** as a discipline of statistics.

This consists of a previous scale in the study of statistics, in which they use probability theory (a branch of mathematics that studies random phenomena) and other branches of mathematics.

Mathematical statistics consists of obtaining information from data and uses mathematical techniques such as mathematical analysis, linear algebra, stochastic analysis, differential equations, etc.

**References**

Statistics. Retrieved from en.wikipedia.org

Parametric statistics. Retrieved from en.wikipedia.org

Non-parametric statistics. Retrieved from en.wikipedia.org