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. the branch of Statistics responsible for making deductions that is, inferring properties, conclusions and trends, from a sample of the set. Your role is to interpret, make projections and comparisons.
Inferential statistics usually uses mechanisms that allow it to carry out these deductions, such as point estimate tests (or confidence intervals), hypothesis tests, parametric tests (such as mean, difference of means, proportions, etc.) and non-parametric (such as the chi-square test, etc.). Correlation and regression analysis, time series, and variance analysis, among others, are also useful.
Therefore, inferential statistics it is extremely useful in population analysis 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 faithfully predicted, nor that we are in the presence of an exact science, but it does mean that it is a possible approximation to the final result.
See also: Probability
Examples of inferential statistics
Some examples of the application of inferential statistics are:
- Voting trend polls Before an important election, various pollsters survey 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 Companies often hire other specialized marketing companies to analyze their market niches through various statistical and differential tools, such as surveys and focus groupsfrom which to deduce which products people prefer and in what context, etc.
- Medical epidemiology Having specific data on the impact of a given population by one or several specific diseases, epidemiologists and public health specialists can reach conclusions regarding what public measures are necessary to prevent these diseases from spreading and contribute to their eradication.
Descriptive statistics
Unlike inferential statistics, descriptive statistics does not worry about conclusions interpretations or hypotheses based on what is reflected by the sample, but rather by the appropriate methods for organizing the information it contains and highlighting its essential characteristics.
In other words, it is “objective” statistics, committed to the presentation of data (textual, graphic or table-based) and the mathematical operations that can be applied to obtain greater data margins, new information or exact frequencies and variabilities.
