We explain what inferential statistics is and its different uses. Also, examples and descriptive statistics.
What is inferential statistics?
It is called inferential statistics or statistical inference to the branch of Statistics in charge of making deductions, that is, inferring properties, conclusions and trends, based on a sample of the whole. Their role is to interpret, make projections and comparisons.
Inferential statistics usually employ mechanisms that allow it to carry out such deductions, such as point estimation tests (or confidence intervals), tests of hypothesis, parametric tests (such as mean, difference of means, proportions, etc.) and non-parametric (such as chi-square test, etc.). The analysis of correlation and regression, time series, analysis of variance, among others.
Hence, inferential statistics is extremely useful in the analysis of populations and trends, to get a possible idea of its actions and reactions in the face of specific conditions. This does not mean that they can be predicted accurately, nor that we are in the presence of a exact science, but a possible approximation to the final result.
Examples of inferential statistics
Some examples of the application of inferential statistics are:
- Voting trend polls. Before an important election, various pollsters poll public opinion to collect relevant data and then, having the sample analyzed and broken down, infer trends: who is the favorite, who is second, etc.
- Market analysis. The Business they often hire other companies specialized in marketing so that they analyze their market niches through various statistical and differential tools, such as surveys andfocus groups, from which to deduce which products people prefer and in what context, etc.
- Medical epidemiology. Having specific data on the affectation of a specific population by one or more specific diseases, epidemiologists and specialists in public health They can reach conclusions about what public measures are necessary to prevent these diseases from spreading and to contribute to their eradication.
Descriptive statistics
Unlike inferential statistics, descriptive statistics are not concerned with conclusions, interpretations or hypotheses based on what is reflected by the sample, but rather with the methods ideal for organizing the information containing and highlighting its essential characteristics.
In other words, it is about "objective" statistics, committed to the presentation of the data (textual, graphical or by tables) and the mathematical operations that can be applied to obtain greater data margins, new information or exact frequencies and variabilities.