**Statistical inference** is a method in which data is used to make predictions about a population using statistics.  

Different types of statistical inference methods are used to predict the parameters of a randomly sampled population. Parameters define how different independent variables are related to each other. These parameters are then used to draw conclusions about the population. 

The steps of making statistical inference are:
1. Make a theory 
2. Generate a hypothesis 
3. Define variables 
4. Choose a population 
5. Formulate a null hypothesis 
6. Take out samples 
7. Use statistical tests on the sample to either accept or reject the null hypothesis 

The different statistical tests that can be used are:
* Confidence Intervals 
* Hypothesis Testing 
* Pearson Correlation 
* Bi-variate regression
* Multi-variate regression
* Chi-square statistics 
* ANOVA or T-test


Statistical inference requires a sample of the population, not the whole population itself. This makes studies more feasible to carry out.



Statistical inference also takes account uncertainty and sample variation into consideration. This allows us to give a range of plausible values of the parameter we are predicting.

There are many applications of statistical inference, not only in computer science, but in the medical and economic sphere as well.   

What is statistical inference? 

Using data, statistical inference predicts population parameters through various methods, considering uncertainty and sample variation, widely applicable in diverse fields.