.

Image Source: Statistical Aid

Sampling is a statistical procedure of selecting some representative part from an existing population or study area. Specifically, draw a sample from the study population using some statistical methods. For example-

if we want to calculate the average age of Bangladeshi people then we can not deal with the whole population. In that time we must have to deal with some representative part of this population. This representative part is called sample and the procedure is called sampling.

It makes possible the study of a large population which contains different characteristics.

It is for economy.

It is for speed.

It is for accuracy.

It saves the sources of data from being all consumed.

Sometimes we can’t work with population such as blood test, in that situation sampling is must.

It is based on the concept of random selection where each population elements have a non-zero chance to occur as a sample. Sampling techniques can be divided into two categories: probability and non-probability. Randomization or chance is the core of probability sampling techniques.

For example, if a researcher is dealing with a population of 100 people, each person in the population would have the odds of 1 out of 100 for being chosen. This differs from non-probability sampling, in which each member of the population would not have the same odds of being selected.

· In opinion poll, a relatively small number of persons are interviewed and their opinions on current issues are solicited in order to discover the attitude of the community as a whole.

· At border stations, customs officers enforce the laws by checking the effects of only a small number of travelers crossing the border.

· A departmental store wises to examine whether it is losing or gaining customers by drawing a sample from its lists of credit card holders by selecting every tenth name.

· In a manufacturing company, a quality control officer take one sample from every lot and if any sample is damage then he reject that lot.

Creates samples that are highly representative of the population.

Sampling bias is tens to zero.

Higher level of reliability of research findings.

Increased accuracy of sample error estimation.

The possibility to make inferences about the population.

Higher complexity compared to non-probability sample.

More time consuming, especially when creating larger sample.

Usually more expensive.

The process of selecting a sample from a population without using statistical probability theory is called non-probability sampling.

Example

Lets say that the university has roughly 10000 students. These 10000 students are our population (N). Each of the 10000 students is known as a unit, but its hardly possible to get known and select every student randomly.

Here we can use Non-Random selection of sample to produce a result.

· It can be used when demonstrating that a particular trait exist in the population.

· It can also be useful when the researcher has limited budget, time and workforce.

· Select samples purposively

· Enable researchers to reach difficult to identify members of the population.

· Lower cost

· Limited time.

Difficult to make valid inference about the entire population because the sample selected is not representative.

We cannot calculate confidence interval.

Comment

- Comment by Tom Wolfer on August 11, 2021 at 4:16am

© 2021 TechTarget, Inc. Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central