What is Sampling?

An introduction to sampling in basic statistics: why studying whole populations is often impractical, how representative samples and adequate sample sizes help, and that samples can be people, countri

Key Takeaways

Sampling makes statistical study practical when surveying an entire population is too costly or slow
A good sample must be representative and sufficiently large
The concept of samples applies to many subjects beyond human populations

What the video covers

The video introduces sampling as a basic concept in statistics
Studying every member of a population, such as all residents of a town, can take a great deal of time
A representative sample aims to reflect the characteristics of the entire population
The sample size must be large enough to better reflect the whole population
Example: a school with 1,000 students considering offering chocolate milk at lunch could survey a sample instead of everyone
Example: studying the correlation between age groups and jobs in a town
Samples are not limited to people; they can be countries, companies, birds, or any object of study
Collecting data from every person is time-consuming even in a small town
The video is produced by GCF Global as part of its educational content

Chapters

Why is sampling used instead of collecting data from everyone?

Collecting data from every person in a population can take a lot of time, even in a small town. Sampling lets researchers study a subset that still provides useful information about the whole.

What makes a sample representative?

A representative sample strives to reflect the characteristics of the entire population. The video also notes that the sample size must be large enough to better reflect the whole.

Can samples include things other than people?

Yes. The video explains that samples can be made up of anything you want to study, such as countries, companies, or even birds.