Description: The inferential model is a statistical approach that allows conclusions to be drawn about a population based on a representative sample. This type of model is based on probability theory and uses statistical techniques to estimate population parameters, test hypotheses, and make predictions. Unlike descriptive statistics, which only describes the characteristics of a dataset, the inferential model seeks to generalize the results obtained from the sample to the broader population. Key features of inferential models include the estimation of confidence intervals, hypothesis testing, and the use of probability distributions. These models are fundamental in scientific research, as they enable researchers to make informed decisions and validate theories based on empirical data. The relevance of inferential models lies in their ability to provide valuable and applicable information across various disciplines, from healthcare to social sciences, facilitating decision-making in uncertain situations.
History: The concept of statistical inference dates back to the 18th century, with significant contributions from mathematicians such as Pierre-Simon Laplace and Carl Friedrich Gauss. However, it was in the 20th century that the use of inferential models was formalized, especially with the work of Ronald A. Fisher, who introduced methods such as analysis of variance and regression. These developments laid the groundwork for modern statistics and its application in various fields.
Uses: Inferential models are used across a wide range of disciplines, including healthcare to assess treatment effectiveness, in social sciences to analyze surveys, and in economics to predict market trends. They are also essential in market research, where they are used to infer consumer preferences from samples.
Examples: A practical example of an inferential model is a clinical study evaluating the effectiveness of a new drug. Researchers select a sample of patients and analyze the results to infer the drug’s effectiveness in the general population. Another example is a national survey seeking to understand citizens’ opinions on a specific topic, using a representative sample to generalize the results.
