Importance of Inferential Statistics

The foundation of Data Science and Machine Learning algorithms are Mathematics and Statistics, and in Statistics, we use two types of statistical methods, Descriptive and Inferential Statistics. 

Inferential Statistics allows you to make decisions based on extrapolation. In that manner, we can fundamentally distinguish Inferential Statistics from Descriptive Statistics, which summarize the measured data. 

There are numerous types of Inferential Statistics, and each is suitable for a specific research design and sample characteristics.

Inferential Statistics uses random samples for testing and, hence, allows us to have confidence that the Sample represents the Population.

Here are some reasons why Inferential Statistics is important:

  1. Infer Population Sample: You can use Inferential Statistics to infer a Sample about the Population. Inferential Statistics aims to draw some conclusions from the Sample and generalize them for population data. It concludes that the Sample selected is statistically significant to the whole Population. It uses measurements from the Sample of subjects in the experiment to compare the treatment groups and generalize the larger Population of subjects.
  2. Compare Models: It can compare two models to find which one is more statistically significant than the other.
  3. Estimation: It frequently involves guessing the characteristics of a population from a sample of the Population and hypothesis testing (i.e., finding evidence for or against an explanation or theory).
  4. Population Characteristics Conclusion: It is the tool that statisticians use to draw conclusions about the characteristics of a population from the characteristics of a sample and to decide how certain they can be of the reliability of those conclusions.
  5. Generalizations of Large Groups: We can use it to make generalizations about large groups, such as estimating average demand for a product, by surveying a sample of consumers' buying habits or predicting future events, such as projecting the future return of a security or asset class based on returns in a sample period.
  6. Feature Selection: whether adding or removing a variable helps improve the model or not.
  7. Null hypothesis Decision: Inferential statistics provide a quantitative method to decide if the null hypothesis should be rejected or not.