We explain what inferential statistics is and its different uses. In addition, examples and descriptive statistics.
What is inferential statistics?
Inferential statistics or statistical inference is called the branch of Statistics responsible for making deductions , that is, inferring properties, conclusions and trends, from a sample of the set. Its role is to interpret, make projections and comparisons.
Inferential statistics usually employ mechanisms that allow you to carry out such deductions, such as point estimation tests (or confidence intervals), hypothesis tests, parametric tests (such as mean, difference of means, proportions, etc.) and not parametric (such as chi-square test, etc.). Also useful are correlation and regression analysis, chronological series, analysis of variance, among others.
Therefore, inferential statistics is extremely useful in the analysis of populations and trends , to get a possible idea of the actions and reactions of the same in the face of specific conditions. This does not mean that they can be predicted faithfully, nor that we are in the presence of an exact science, but of a possible approximation to the final result.
Examples of inferential statistics
Some examples of the application of inferential statistics are:
- S voting trends . Before an important election, several pollsters poll public opinion to gather relevant data and then, having the sample analyzed and broken down, infer trends: who is the favorite, who is second, etc.
- Market analysis . The companies often hire other companies specializing in marketing to analyze their market niches through various statistics and differential tools, such as surveys and focus groups , from which figure out what products people prefer and in what context, etc.
- Medical epidemiology . Having the specific data on the involvement of a population determined by one or several specific diseases, epidemiologists and public health specialists can reach conclusions as to what public measures are necessary to prevent such diseases from spreading and contribute to their eradication.
Unlike the inferential one, descriptive statistics do not care about conclusions , interpretations or hypotheses based on what is reflected in the sample, but rather about the ideal methods for organizing the information it contains and highlighting its essential characteristics.
In other words, it is the “objective” statistics, committed to the presentation of data (textual, graphic or by tables) and the mathematical operations that can be applied to obtain greater data margins, new information or frequencies and exact variability.