# Sampling Task

(Redirected from Sampling (Statistics))

A Sampling Task is a selection task that requires a subset of a set.

**Context:**- It can be solved by a Sampling System (that implements a Sampling algorithm).
- It can range from being a Random Sampling Task to being a Non-Random Sampling Task.

**Example(s):****Counter-Example(s):**- ...

**See:**Skewed Data, Quality Assurance, Statistical Survey, Population (Statistics), Observation, Survey Sampling, Sample (Statistics), Stratified Sampling, Probability Theory.

## References

### 2014

- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/sampling_(statistics) Retrieved:2014-8-26.
- In statistics, quality assurance, & survey methodology,
**sampling**is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Each observation measures one or more properties (such as weight, location, color) of observable bodies distinguished as independent objects or individuals. In survey sampling, weights can be applied to the data to adjust for the sample design, particularly stratified sampling. Results from probability theory and statistical theory are employed to guide practice. In business and medical research, sampling is widely used for gathering information about a population.The sampling process comprises several stages:

- Defining the population of concern
- Specifying a sampling frame, a set of items or events possible to measure
- Specifying a sampling method for selecting items or events from the frame
- Determining the sample size
- Implementing the sampling plan
- Sampling and data collecting
- Data which can be selected

- In statistics, quality assurance, & survey methodology,

### 2017

- (Statistics Solutions, 2017) ⇒ http://www.statisticssolutions.com/sample-size-calculation-and-sample-size-justification/sampling/ Retrieved: 2017-10-18.
**Sampling**is a statistical procedure that is concerned with the selection of the individual observation. In sampling, it is assumed that samples are drawn from the population and sample means and populations means are equal. A population can be defined as a whole that includes all items and characteristics of the research taken into study. However, gathering all the needed information is time consuming and costly. Thus, inferences of the population are made through samples.

- Random sampling:

- In data collection, every individual observation has equal probability to be selected into a sample. In random sampling, there should be no pattern when drawing a sample.

- Significance: Significance is the percent of chance that a relationship may be found in sample data due to luck. Researchers often use the 0.05% significance level.

- Probability and non-probability sampling:

- Probability sampling is the sampling technique in which every individual unit of the population has greater than zero probability of getting selected into a sample.

- Non-probability sampling is the sampling technique in which some elements of the population have no probability of getting selected into a sample.

### 2014

- (Wikipedia, 2017) ⇒ http://en.wikipedia.org/wiki/sampling_(statistics) Retrieved:2017-10-18.
- Within any of the types of frames identified above, a variety of sampling methods can be employed, individually or in combination. Factors commonly influencing the choice between these designs include:

- Nature and quality of the frame
- Availability of auxiliary information about units on the frame
- Accuracy requirements, and the need to measure accuracy
- Whether detailed analysis of the sample is expected
- Cost/operational concerns

- The types of sampling method are:

- Simple Random Sampling
- Systematic Sampling
- Stratified Sampling
- Probability-proportional-to-size Sampling
- Cluster Sampling
- Quota Sampling
- Minimax Sampling
- Accidental Sampling
- Voluntary Sampling
- Line-Intercept Sampling
- Panel Sampling
- Snowball Sampling
- Theoretical Sampling