What is Estimation?
Estimation in statistics involves using sample data to make educated guesses about a population’s characteristics, such as mean, variance, or proportion. The population refers to the entire interest group, like all people in a country or all products made by a company.
Estimation in Statistics
Since it’s often impractical to measure every member of a population, statisticians rely on samples to make inferences about the entire population. Estimation helps to conclude population parameters based on sample data.
Estimation in Statistics
Estimation is a technique for calculating information about a bigger group from a smaller sample, and statistics are crucial to analyzing data. For instance, the average age of a city’s population may be obtained by taking the age of a sample of 1,000 residents. While estimates aren’t perfect, they are typically trustworthy enough to be of value.
In this article, we examine the significance of statistics, their function in the interpretation of data, and how efficient data analysis leads to the making of decisions based on accurate information.
Table of Content
- What is Estimation?
- Purpose of Estimation in Statistics
- Types of Estimation
- Point Estimation
- Interval Estimation
- Examples of Estimation in Statistics
- Estimation Methods
- 1. Method of Moments
- 2. Maximum Likelihood Estimation (MLE)
- Estimators as Random Variables
- Factors Affecting Estimation
Contact Us