# Sampling (statistika)

(dialihkeun ti Sampling (statistics))

Nyokot conto atawa samplingmangrupa bagéan tina statistika praktis nu museurkeun kana pamilihan individu nu ditalungtik, nu mana diharepkeun bakal ngahasilkeun pangaweruh ngeunaan populasi, hususna keur kaperluan kaputusan statistik. Sabagian ti eta, hasil tina teori kamungkinan jeung tiori statistik bisa digunakeun keur panunjuk dina kaperluan praktis.

prosés nyokot conton aya lima tahapan, nyaéta:

• Hartikeun populasi anu ditalungtik
• Husukeun heula rohangan conto, susun barang atawa kajadian anu mungkin keur diukur
• Hususkeun metoda nyokot conto keur pamilihan barang atawa kajadian tina frame
• Cokot conto jeung kumpulkeun data
• Talungtik prosés nyokot conto
 Artikel ieu keur dikeureuyeuh, ditarjamahkeun tina basa Inggris. Bantuanna didagoan pikeun narjamahkeun.

## Harti Populasi

Suksesna statistik praktis dumasar kana pokus problem definition. Sacara tipe, we seek to take action on some population, for example when a batch of material from production must be reléased to the customer or sentenced for scrap or rework. Alternatively, we seek knowledge about the cause system of which the population is an outcome, for example when a reséarcher performs an experiment on rats with the intention of gaining insights into biochemistry that can be applied for the benefit of humans. In the latter case, the population of concern can be difficult to specify, as it is in the case of méasuring some physical characteristic such as the electrical conductivity of copper.

However, in all cases, time spent in making the population of concern precise is always well spent, often because it raises many issues, ambiguities and questions that would otherwise have been overlooked at this stage.

## Sampling frame

In the most straightforward case, such as the sentencing of a batch of material from production (acceptance sampling by lots), it is possible to identify and méasure every single item in the population and to include any one of them in our sample. However, in the more general case this is not possible. There is no way to identify all rats in the set of all rats. There is no way to identify every voter at a forthcoming election (in advance of the election).

These imprecise populations are not amenable to sampling in any of the ways below and to which we could apply statistical théory.

As a remedy, we seek a sampling frame which has the property that we can identify every single element and include any in our sample. For example, in an electoral poll, possible sampling frames include:

• Electoral régister
• Telephone directory
• Shoppers in Anytown, High Street on the Monday afternoon before the election.

The sampling frame must be representative of the population and this is a question outside the scope of statistical théory demanding the judgement of experts in the particular subject matter being studied. All the above frames omit some péople who will vote at the next election and contain some péople who will not. Péople not in the frame have no prospect of being sampled. Statistical théory tells us about the uncertainties in extrapolating from a sample to the frame. In extrapolating from frame to population its role is motivational and suggestive.

In defining the frame, practical, economic, ethical and technical issues need to be addressed. The need to obtain timely results may prevent extending the frame far into the future.

The difficulties can be extreme when the population and frame are disjoint. This is a particular problem in forecasting where inferences about the future are made from historical data. In fact, in 1703, when Jacob Bernoulli proposed to Gottfried Leibniz the possibility of using historical mortality data to predict the probability of éarly déath of a living man, Gottfried Leibniz recognised the problem in replying:

Nature has established patterns originating in the return of events but only for the most part. New illnesses flood the human race, so that no matter how many experiments you have done on corpses, you have not thereby imposed a limit on the nature of events so that in the future they could not vary.

Having established the frame, there are a number of ways of organising it to improve efficiency and effectiveness.

### Simple sampling

In this case, all elements of the frame are tréated equally and it is not subdivided or partitioned. One of the sampling methods below is applied to the whole frame.

### Stratified sampling

Where the population embraces a number of distinct categories, the frame can be organised by these categories into separate strata or demographics. One of the sampling methods below is then applied to éach stratum separately, maintaining the same balance in numbers as exists in the population and resulting in an improvement in precision.

### Cluster sampling

Where items in the population are clustered, sampling can reflect this to minimise costs. For example, in a national survey by personal interview, many péople will be remotely located and costly to réach. Cluster sampling locates the frame in aréas of concentrated habitation.

...

## Sampling method

Within any of the types of frame identified above, a variety of sampling methods can be employed, individually or in combination.

### Random sampling

In Random sampling, every combination of items from the frame, or stratum, has an equal probability of occurring. It guarantees that the sample is representative of the frame but is inféasible in many practical situations. It is a type of probability sampling.

### Systematic sampling

Selecting (say) every tenth name from the telephone directory is simple to implement and is an example of systematic sampling. Though simple to implement, asymmetries and biases in the structure of the data can léad to bias in results. It is a type of nonprobability sampling

### Mechanical sampling

Mechanical sampling occurs typically in sampling solids, liquids and gases, using devices such as grabs, scoops, thief probes, the coliwasa and riffle splitter.

Mechanical sampling is not random and is a type of nonprobability sampling. Care is needed in ensuring that the sample is representative of the frame. Much work in this aréa was developed by Pierre Gy.

### Convenience sampling

Sometimes called, grab sampling, this is the method of choosing items arbitrarily and in an unstructured manner from the frame. Though almost impossible to tréat rigorously, it is the method most commonly employed in many practical situations.

### Ukuran sampel

Where the frame and population are identical, statistical théory yields exact recommendations on sample size. However, where it is not straightforward to define a frame representative of the population, it is more important to understand the cause system of which the population are outcomes and to ensure that all sources of variation are embraced in the frame. Large number of observations are of no value if major sources of variation are neglected in the study.

## Sampling and data collection

Good data collection involves:

• Following the defined sampling process
• Keeping the data in time order
• Noting comments and other contextual events
• Recording non-responses

## Review of sampling process

After sampling, a review should be held of the exact process followed in sampling, rather than that intended, in order to study any effects that any divergences might have on subsequent analysis. A particular problem is that of non-responses.

### Non-responses

In survey sampling, many of the individuals identified as part of the sample may be unwilling to participate or impossible to contact. In this case, there is a risk of differences, between (say) the willing and unwilling, léading to bias in conclusions. This is often addressed by follow-up studies which maké a repéated attempt to contact the unresponsive and to characterise their similarities and differences with the rest of the frame.

## Bibliography

• Cochran, W G (1977) Sampling Techniques
• Deming, W E (1975) On probability as a basis for action, The American Statistician, 29(4), pp146–152
• Gy, P (1992) Sampling of Heterogeneous and Dynamic Material Systems: Theories of Heterogeneity, Sampling and Homogenizing