In statistics, sample refers to a set of data collected from a larger set called population. In other words, a sample is a subset of population. A sample can be collected either randomly or through systematic methods.
Importance of Sample
In data analysis, often the population consists of large data and evaluating them for a specific purpose is a complex & tedious process. Hence statisticians, collect a data sample which is representative of the population. The properties of the sample can then be attributed to that of the population. Sampling is an important process for any business as it takes into consideration a homogeneous or similar behaving smaller group from the larger population. In business, it help identify the behavior of a larger population by analysing the smaller group.
The process of selecting this sample from a population is called as sampling. There are various reasons why sampling is done:
1. Sampling saves time
2. In case of destructive testing methods, sampling is useful
3. In cases when the entire population data cannot be accessed, sampling is useful
Types of Samples
There are basically two types depending on how they are selected.
1. Random Sample – Here the probability of selecting an object from a population is same for all the objects
2. Non Random Sample – It is selected based on a certain criteria where all the objects from a population do not have the same probability of selection.
Hence, this concludes the definition of Sample along with its overview.
This article has been researched & authored by the Business Concepts Team. It has been reviewed & published by the MBA Skool Team. The content on MBA Skool has been created for educational & academic purpose only.
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