Statistical inference is the procedure of applying data analysis to comprehend the primary probability distribution. Analyzing data with inference statistically enables the estimation of properties of populations, for example by testing hypotheses. The observed data set is believed to represent the sampling of a larger population. Statistical inference allows the application to analyze sample data to evaluate population parameters. Statistical inference assumes that each member of the population of interest has an equal chance of being represented in a particular sample. When the sample is not randomly selected. The study findings can still be generalized if the sample is representative of the whole population of interest. A set of data analytical methods used to infer population parameters assume sample estimates follow a bell-shaped distribution, called a normal distribution. The primary goal of statistical inference is to determine and recognize the variation or uncertainty of our findings results may differ if we repeat the study, or how unresolved our research is, permit us to take this uncertainty and variations. It is very important to use inferential statistics to examine the data properly. To make a more authentic and accurate conclusion, proper data analysis is important to interpret research results. Statistical inference includes an extensive range of applications in different fields, such as: Business Analysis
Artificial Intelligence
Financial Analysis
Fraud Detection
Machine Learning
Share Market
Pharmaceutical Sector