Different types of **samples **are obtained depending on the method by which **elementary units **are selected for **observation**. These include two major types of **samples**, which are: **non-probability samples** and **probability samples**. **Non-probability samples **include: **convenience samples**, and **judgement samples**; while **probability samples **include various forms of **random samples**.

# Non-probability Samples

## Convenience Samples

A **convenience sample **(also known as **accidental sample**, **grab sample**, or **availability sample**) is a **non-probability sample**, constituted by selecting the most conveniently located or easily accessible **elementary units**. This kind of samples is preferred by many researchers because it can be accessed fast, inexpensive, easy and the subjects are readily available.

Examples of a **convenient sample **is student volunteers as subjects for the research, subjects that are selected from a clinic, a class or an institution that is easily accessible to the **researcher**. One disadvantage of this type of **samples **is that, it is unlikely that a **convenience sample **of a **statistical population **is representative of it in the sense that **valid inferences **can be drawn from the **sample **about the **population**.

## Judgement Samples

A **judgement sample **(also called **purposive sample **or **authoritative sample**) is a **non-probability sample**, where the **researcher **chooses the **elementary units **using personal judgement based on prior experience. **Judgment sample **is most effective when only a limited number of individuals possess the trait that a researcher is interested in.

For example, a researcher may decide to draw the entire sample from one “representative” city, even though the population includes all cities.

# Probability Samples

## Random Samples

Most important, because it avoids the problem of unrepresentativeness, is the **random sample**, or **probability sample**, which is a subset of all **elementary units**, or of an associated **population **of their characteristics, that is chosen by a **random **process that gives each unit of the **frame **or associated **population **a known positive (but not necessarily equal) chance to be selected. If properly executed, the **random **selection process allows the investigator no discretion as to which particular units in the **frame **or **population **enter the **sample**. As a result, such a **sample** tends to maximize our chances of making **valid inferences **about totality from which it is drawn.

Because **random samples **are so important, we must look at them in some detail. Many types of **random samples **exist; the most important ones are introduced below.

### Simple Random Sampling

A **simple random sample **is a **subset **of a **frame**, or of an associated **population**, chosen in such a fashion that every possible **subset **of like size has an equal chance of being selected. This procedure implies that each individual **unit **of the **frame **or **population **has an equal chance of selection as well.

### The Systematic Random Sampling

The **systematic random sample **is a **subset **of a **frame**, or of an associated **population**, chosen by randomly selecting one of the first *k* **elements **and then including every *k*^{th} **element **thereafter. If this procedure is employed, *k* is determined by dividing **population **size, *N*, by desired **sample **size, *n*.

### The Stratified Random Sample

Sometimes the **frame **or **population **to be sampled is known to contain two or more mutually exclusive and clearly distinguishable subgroups or **strata **that differ greatly from one another with respect to some characteristic of interest, while the **elements **within each **stratum **are fairly homogeneous. In such circumstances, one can select a **stratified random sample**, which is a **subset **of a **frame**, or of an associated **population**, chosen by taking separate (**simple **or **systematic**) **random samples **from every **stratum **in the **frame **or **population**, often in such a way that the sizes of the separate **samples **vary with the importance of the different **strata**.

### The Clustered Random Sample

Finally, there are occasions when the **frame** or **population **to be sampled is naturally subdivided into **clusters **on the basis of physical accessibility. In such circumstances, one can select a **clustered random sample**, which is a **subset **of a **frame**, or of an associated **population**, chosen by taking separate **censuses **in a randomly chosen subset of geographically distinct **clusters**.

Someone who wanted to **sample **the residents or shops of a city, for example, might divide the city into blocks, randomly select a few of these (by any of the methods previously mentioned), and then interview every resident or shop owner within the chosen blocks.

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