Statistics Definitions > Point Estimate

## What is a Point Estimate

In simple terms, any statistic can be a point estimate. A statistic is an estimator of some parameter in a population. For example:

- The sample standard deviation (s) is a point estimate of the population standard deviation (σ).
- The sample mean (̄x) is a point estimate of the population mean, μ
- The sample variance (s
^{2}is a point estimate of the population variance (σ^{2}).

In more formal terms, the estimate occurs as a result of point estimation applied to a set of sample data. Points are single values, in comparison to interval estimates, which are a range of values. For example, a confidence interval is one example of an interval estimate.

## Finding the Estimates

Four of the most common ways to find an estimate:

**The Method of Moments**: is based on the law of large numbers and uses relatively simple equations to find point estimates. Is often not too accurate and has a tendency to be biased.*More info*.**Maximum Likelihood:**uses a model (for example, the normal distribution) and uses the values in the model to maximize a likelihood function. This results in the most likely parameter for the inputs selected.*More info.***Bayes Estimators:**minimize the average risk (an expectation of random variables).*More info*.**Best Unbiased Estimators:**several unbiased estimators can be used to approximate a parameter. Which one is “best” depends on what parameter you are trying to find. For example, with variance, the estimator with the smallest variance is “best”.*More info*.

If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.

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