Purpose of Estimation in Statistics
Statistical estimation is essential for making inferences about populations using sample data, helping to determine parameters like mean and variance without individual measurements.
- This evaluation is vital for decision-making in business and healthcare, informing strategies and treatment options.
- It is closely linked to hypothesis testing, contributing to scientific development, political decisions, public health, and economic choices.
- Risk assessment benefits from evaluation in managing probabilities and risk in finance and insurance.
- Quality control also relies on evaluation to ensure products and services meet standards by identifying and correcting deviations.
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