# Sample (statistics)

**samplesamplesstatistical samplesampleddata samplenpopulationsample sizesamplingsample data**

In statistics and quantitative research methodology, a data sample is a set of data collected and the world selected from a statistical population by a defined procedure.wikipedia

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### Statistics

**statisticalstatistical analysisstatistician**

In statistics and quantitative research methodology, a data sample is a set of data collected and the world selected from a statistical population by a defined procedure.

When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples.

### Statistic

**sample statisticempiricalmeasure**

Samples are collected and statistics are calculated from the samples, so that one can make inferences or extrapolations from the sample to the population.

A statistic (singular) or sample statistic is any quantity computed from values in a sample, often the mean.

### Statistical population

**populationsubpopulationsubpopulations**

In statistics and quantitative research methodology, a data sample is a set of data collected and the world selected from a statistical population by a defined procedure. The data sample may be drawn from a population without replacement (i.e. no element can be selected more than once in the same sample), in which case it is a subset of a population; or with replacement (i.e. an element may appear multiple times in the one sample), in which case it is a multisubset.

In statistical inference, a subset of the population (a statistical sample) is chosen to represent the population in a statistical analysis.

### Simple random sample

**random samplingsimple random samplingsampling without replacement**

Several types of random samples are simple random samples, systematic samples, stratified random samples, and cluster random samples.

In statistics, a simple random sample is a subset of individuals (a sample) chosen from a larger set (a population).

### Data set

**datasetdatasetsdata sets**

In statistics and quantitative research methodology, a data sample is a set of data collected and the world selected from a statistical population by a defined procedure.

### Convenience sampling

**convenience sampleAccidental samplingConvenience Samples**

Some examples of nonrandom samples are convenience samples, judgment samples, purposive samples, quota samples, snowball samples, and quadrature nodes in quasi-Monte Carlo methods.

Convenience sampling (also known as grab sampling, accidental sampling, or opportunity sampling) is a type of non-probability sampling that involves the sample being drawn from that part of the population that is close to hand.

### Judgment sample

Some examples of nonrandom samples are convenience samples, judgment samples, purposive samples, quota samples, snowball samples, and quadrature nodes in quasi-Monte Carlo methods.

Judgment sample, or Expert sample, is a type of random sample that is selected based on the opinion of an expert.

### Estimation theory

**parameter estimationestimationestimated**

The first is a statistical sample – a set of data points taken from a random vector (RV) of size N.

### Independent and identically distributed random variables

**independent and identically distributedi.i.d.iid**

In mathematical terms, given a probability distribution F, a random sample of length n (where n may be any positive integer) is a set of realizations of n independent, identically distributed (iid) random variables with distribution F.

In statistics, it is commonly assumed that observations in a sample are effectively i.i.d.. The assumption (or requirement) that observations be i.i.d. tends to simplify the underlying mathematics of many statistical methods (see mathematical statistics and statistical theory).

### Sample size determination

**sample sizeSampling sizessample**

Sample size determination is the act of choosing the number of observations or replicates to include in a statistical sample.

### Probability distribution

**distributioncontinuous probability distributiondiscrete probability distribution**

In mathematical terms, given a probability distribution F, a random sample of length n (where n may be any positive integer) is a set of realizations of n independent, identically distributed (iid) random variables with distribution F.

When a sample (a set of observations) is drawn from a larger population, the sample points have an empirical distribution that is discrete and that provides information about the population distribution.

### Sampling (statistics)

**samplingrandom samplesample**

The best way to avoid a biased or unrepresentative sample is to select a random sample, also known as a probability sample.

In statistics, quality assurance, and survey methodology, sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of the whole population.

### Quantitative research

**quantitativequantitative methodsquantitative data**

In statistics and quantitative research methodology, a data sample is a set of data collected and the world selected from a statistical population by a defined procedure.

### Statistical unit

**experimental unitunitsexperimental units**

The elements of a sample are known as sample points, sampling units or observations.

### Census

**UK censuscensusespopulation census**

Typically, the population is very large, making a census or a complete enumeration of all the values in the population either impractical or impossible.

### Enumeration

**enumeratedenumerativelist**

Typically, the population is very large, making a census or a complete enumeration of all the values in the population either impractical or impossible.

### Inference

**inferredinferlogical inference**

Samples are collected and statistics are calculated from the samples, so that one can make inferences or extrapolations from the sample to the population.

### Extrapolation

**extrapolateextrapolatedextrapolating**

Samples are collected and statistics are calculated from the samples, so that one can make inferences or extrapolations from the sample to the population.

### Subset

**supersetproper subsetsubsets**

The data sample may be drawn from a population without replacement (i.e. no element can be selected more than once in the same sample), in which case it is a subset of a population; or with replacement (i.e. an element may appear multiple times in the one sample), in which case it is a multisubset.

### Systematic sampling

**systematic methodsystematic random samplingsystematic samples**

Several types of random samples are simple random samples, systematic samples, stratified random samples, and cluster random samples.

### Stratified sampling

**stratificationstratifiedstratified random sampling**

Several types of random samples are simple random samples, systematic samples, stratified random samples, and cluster random samples.

### Cluster sampling

**clustercluster random samplescluster sample**

### Sampling bias

**ascertainment biasbiased samplebias**

A sample that is not random is called a non-random sample or a non-probability sampling.

### Nonprobability sampling

**purposive samplingnon-probability samplingjudgmental sampling**

Some examples of nonrandom samples are convenience samples, judgment samples, purposive samples, quota samples, snowball samples, and quadrature nodes in quasi-Monte Carlo methods. A sample that is not random is called a non-random sample or a non-probability sampling.

### Quota sampling

**quota methodquota sampleQuota Samples**

Some examples of nonrandom samples are convenience samples, judgment samples, purposive samples, quota samples, snowball samples, and quadrature nodes in quasi-Monte Carlo methods.