Statistics can be a complex form of mathematics, with many complicated terms that might be somewhat unknown, or in the least confusing, to the laymen.

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One such term is a ‘sample’, which is an important part of statistical analysis and used in countless ways to find specific answers within a stream of numerical values.

But what exactly is a ‘sample’, and how are they used within statistical analysis?

**What Are ‘Samples’? **

Within statistics, a ‘sample’ is an analytic subset of a larger population – that is, a smaller study group within a larger complete group of people.

This could mean that when working within a specific neighborhood or town, you only take into account the residents of a specific street – using their numerical characteristics (such as age etc.) to garner an intimate understanding of the demographics of the area in general.

**How Are Samples Used? **

Of course, there are many reasons that statistical information is collected from various populations. It could be for a national census, or it could be a proposed canvassing project based on an area’s political persuasion.

**To Stay Relevant **

The main way samples are used is as a means of collecting relevant information.

The smaller sample area means that it is much easier to keep information applicable, and to sift through any anomalies and unwanted fields of information.

**To Be Specific**

Samples also make it easier to focus on specific fields of information – such as the age ranges of a neighborhood etc.

On the flip side, it also gives survey takers the freedom to collect more fields of information pertaining to the smaller sample group as well.

This could be if it is a more focused survey or census, wherein multiple topics of numerical information are needed to be taken.

This freedom is what makes samples useful to survey takers, as it makes either approach manageable, effective, and simpler to achieve.

**What Sample-Taking Methods Are There? **

When taking a sample during a survey, there are several approaches that survey takers can use to get the results they want – separated into probability and non-probability sampling.

**Probability Sampling**

Within probability sampling, there are four main approaches that can be used both to choose, and collect the sample data.

The first is ‘simple random sampling’, which means that everyone within a specific sample area has the same chance of being selected – thus making the probability of achieving accurate, fair information much higher.

The second is ‘systematic sampling’, where each member of a sample population is assigned a number, and one is chosen at regular intervals – depending on a chosen pattern (eg house numbers 6,16,26 etc).

The third is ‘stratified sampling’, which involves separating the sample population into subsects depending on key, defining characteristics.

These could be based on gender, age and so on, and this can be a good way of honing a sample survey.

The fourth is ‘cluster sampling’, which separates the sample population into subgroups, but instead of selecting individuals at random, it selects entire groups to examine.

**Non-Probability Sampling**

There are also four subsections within non-probability sampling methods.

The first is ‘convenience sampling’, which involves surveying subjects who happen to be most accessible to the surveyor. This could be fellow students in a university, or colleagues at work.

The second method is ‘voluntary response sampling’, which involves a bulletin being advertised, and members of the public randomly volunteering.

These results are always inherently biased, because it is only specific types of people who are always likely to volunteer.

The third method is ‘purposive sampling’, which involves the researcher selecting a sample group based on their relevance and suitability for the survey.

The fourth method within this group is ‘snowball sampling’, which involves the recruitment of participants via other participants.

This could be through offering free gifts, vouchers, or some other incentive to get people to suggest it to their friends, family, and colleagues.

**What Are The Benefits? **

Whatever the point of the survey is, samples are used to achieve a series of specific procedural outcomes.

**Manageable Focus**

The main benefit of taking a sample during population statistics is that you have a more manageable number of people (and their information) to handle at any one time.

This way you can get a clear sense of the demographics of specific areas, without having to include every single person within a particular area.

**Saving Time**

Taking a sample during a survey is also a good time saving technique, as it means you can gauge the key factors at play within an area, without having to ask every single person in the entire city or county.

**Sparing Resources**

It also means that less survey takers are required, as well as analysts (Also check out the What Is Political Data Analysis) to pour through the data once it has been collected.

If the data is more manageable, then fewer people can work on any one project, and more can get done in a shorter amount of time.

This is particularly handy if you are taking specific surveys nationwide, or handling multiple survey projects.

**Saving Money**

Also, survey taking costs money – both in terms of compiling surveys, printing the surveys, and paying the people to do their specific tasks.

If there are more people in play, handling a larger survey, then the cost will be higher.

Similarly, a longer, more in depth project could cost more money the longer it progresses – especially if it takes priority over other surveying opportunities.

**Final Thoughts**

And there we have it, everything you need to know about ‘samples’ within statistics, and the ways they are used to achieve certain outcomes.

There are many benefits to using samples within statistical analysis, many of them coming down to ease and manageability, allowing survey takers to hone their resources, money, and time, to get a more accurate array of data in a much more efficient time frame (Also check out the How To Clean Data).

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