# Point estimation

point estimatepointpoint estimatorestimatedpoint estimates
In statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population mean).wikipedia
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### Estimator

estimatorsestimateestimates
More formally, it is the application of a point estimator to the data to obtain a point estimate.
There are point and interval estimators.

### Confidence interval

confidence intervalsconfidence levelconfidence
Point estimation can be contrasted with interval estimation: such interval estimates are typically either confidence intervals, in the case of frequentist inference, or credible intervals, in the case of Bayesian inference.
Interval estimation can be contrasted with point estimation.

### Interval estimation

interval estimateintervalInterval (statistics)
Point estimation can be contrasted with interval estimation: such interval estimates are typically either confidence intervals, in the case of frequentist inference, or credible intervals, in the case of Bayesian inference.
In statistics, interval estimation is the use of sample data to calculate an interval of possible values of an unknown population parameter; this is in contrast to point estimation, which gives a single value.

### Maximum likelihood estimation

maximum likelihoodmaximum likelihood estimatormaximum likelihood estimate
The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate.

### Maximum a posteriori estimation

maximum a posterioriMAPposterior mode
The MAP can be used to obtain a point estimate of an unobserved quantity on the basis of empirical data.

### Statistics

statisticalstatistical analysisstatistician
In statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population mean).

### Sample (statistics)

samplesamplesstatistical sample
In statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population mean).

### Data

In statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population mean).

### Point (geometry)

pointpointslocation
In statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population mean).

### Parameter space

weight space
In statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population mean).

### Parameter

parametersparametricargument
In statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population mean).

### Mean

mean valueaveragepopulation mean
In statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population mean).

### Frequentist inference

frequentistfrequentist statisticsclassical
Point estimation can be contrasted with interval estimation: such interval estimates are typically either confidence intervals, in the case of frequentist inference, or credible intervals, in the case of Bayesian inference.

### Credible interval

credible intervalscredible regionBayesian interval
Point estimation can be contrasted with interval estimation: such interval estimates are typically either confidence intervals, in the case of frequentist inference, or credible intervals, in the case of Bayesian inference.

### Bayesian inference

BayesianBayesian analysisBayesian method
Point estimation can be contrasted with interval estimation: such interval estimates are typically either confidence intervals, in the case of frequentist inference, or credible intervals, in the case of Bayesian inference.

### Minimum-variance unbiased estimator

minimum variance unbiased estimatorbest unbiased estimatorUMVU

### Loss function

objective functioncost functionrisk function

### Gauss–Markov theorem

best linear unbiased estimatorGauss–Markovbest linear unbiased estimation

### Median

averagesample medianmedian-unbiased estimator

### Method of moments (statistics)

method of momentsmethod of matching momentsmethod of moment matching

### Posterior probability

posterior distributionposteriorposterior probability distribution
The Minimum Message Length (MML) point estimator is based in Bayesian information theory and is not so directly related to the posterior distribution.

### Central tendency

LocalityLocality (statistics)Measure of central tendency