Population and Sample in Statistics



Population and Sample

Lets discuss about how large collections of data is targeted and grouped in statistics ..  

Population:

A population is any large collection of objects or individuals, such as  students, animals or trees about which information is desired.

Example: 
  1. Packet of Food grains
  2. A group of people suffering from a particular disease
  3. Collection of books

Sample:

Sample is the representative unit of the target population, which is worked upon by the researchers. 

Example:

While purchasing food grains, we inspect only a handful of grains and draw conclusions about the quality of the whole lot.

Note: In this case handful of grains is a sample and the whole lot is a population.

 Introduction to Sampling

  • Sampling is the method of selecting the number of individuals or objects in such a way that it represents the whole population.
  • A sample is used to find out the characteristics of the population.
  • The purpose of the sampling is to gather data in order to make inferences and make decisions about the population.

Sampling considerations

  • Larger sample sizes are more accurate representations of the whole population.
  • The sample size chosen is a balance between obtaining a statistically valid representation and the time, energy, money, labor, equipment and access available.
  • A sampling strategy made with the minimum of bias is the most statistically valid.


     

Advantages of sampling 

  1. Low cost 
  2. Less time consuming
  3. Suitable in limited resources

Disadvantages of Sampling

  1. Difficult to select a truly representative sample
  2. It is important to have subject specific knowledge
  3. Chances of bias
  4. Sampling is impossible when population is too small and heterogeneous 

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