Point estimation is a statistical method used to estimate an unknown parameter of a population based on sample data. It involves choosing a single value, called a point estimator, to represent the parameter of interest. The point estimator serves as the best guess for the true parameter.
The main objective of point estimation is to provide a single value, known as a point estimate, that serves as the best guess or approximation of an unknown population parameter. This parameter could be something like the population mean, variance, or proportion.
Point estimation uses data from a sample - since measuring an entire population is often impractical - to calculate this single value. For example, if we want to estimate the average height of all adults in a country, we might collect a sample of 1000 adults, calculate the sample mean, and use that as the point estimate for the population mean. Ideally, a good point estimate should be unbiased (meaning its expected value matches the true population parameter) and have low variance (meaning the estimates are consistently close to the true value).
This point estimate can then be used for various purposes, such as summarizing data, hypothesis testing, or building confidence intervals.
Comments