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Statistical Inference

Descriptive statistics are the statistical methods used for describing a collection of data, while the statistical inference is the procedure applied to drawing conclusions from data that are subject to random variation. In statistics, the process of drawing conclusions from data that are subject to random variation is called Statistical Inference.

In particular, the statistical inferences are playing vital part in examining various system of procedures which are useful for come up with suitable conclusions derived from a dataset gathered from systems which are generally affected by random variation such as observational errors, random sampling, or random experimentation.

There are numerous system of procedures are available for inference and induction. As per the initial requirements of this system, one can produce sensible conclusions/answers by applying such system of procedures in any well-defined situation. Moreover, the conclusion derived by this procedure for a well-defined situation would be generalized for various situations. Inferential Statistics are widely used in testing predefined hypotheses and also used to make estimations based on sample data.

Inferential statistics are mainly used in testing a hypothesis and drawing conclusions about a population, based on the corresponding sample data. Statistical inference is suitable for many situations to make desirable conclusions from the corresponding dataset and many approaches are available to perform such inference. There are many tools available for the Estimation of Parameters, where the most straightforward and easiest method to understand is "Likelihood Ratios".