# 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.

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

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