Inferential statistics is a branch of statistics that involves using a sample of data to make generalizations or predictions about a larger population. The goal of inferential statistics is to use the information gathered from a sample to draw conclusions about the population from which the sample was drawn.
Inferential statistics involves using probability theory to make educated guesses about the population parameters based on the information gathered from a sample. The two most common types of inferential statistics are estimation and hypothesis testing.
Estimation involves using the information gathered from a sample to estimate the value of a population parameter. For example, a researcher might use a sample of the population to estimate the average income of the entire population.
Hypothesis testing involves using the information gathered from a sample to test a hypothesis about a population parameter. For example, a researcher might use a sample of the population to test whether the average income of the entire population is significantly different from a specific value.
Inferential statistics is an important tool in many fields, including business, medicine, social sciences, and many others. It allows researchers and analysts to make informed decisions based on the data available to them, even when they do not have access to information about the entire population.