Branches of Statistical Inference
There are two main branches of statistical inference:
- Parameter Estimation
- Hypothesis Testing
Parameter Estimation
Parameter estimation is another primary goal of statistical inference. Parameters are capable of being deduced; they are quantified traits or properties related to the population you are studying. Some instances comprise the population mean, population variance, and so on-the-list. Imagine measuring each person in a town to realize the mean. This is a daunting if not an impossible task. Thus, most of the time, we use estimates.
There are two broad methods of parameter estimation:
- Point Estimation
- Interval Estimation
Hypothesis Testing
Hypothesis testing is used to make decisions or draw conclusions about a population based on sample data. It involves formulating a hypothesis about the population parameter, collecting sample data, and then using statistical methods to determine whether the data provide enough evidence to reject or fail to reject the hypothesis.
Statistical Inference
Statistical inference is the process of using data analysis to infer properties of an underlying distribution of a population. It is a branch of statistics that deals with making inferences about a population based on data from a sample.
Statistical inference is based on probability theory and probability distributions. It involves making assumptions about the population and the sample, and using statistical models to analyze the data. In this article, we will be discussing it in detail.