Sampling Techniques : in this video, you will learn how to sample a population using various methods such as random sampling, systematic sampling, and stratified sampling.
– Intro (0:00)
– Probability sampling (1:51)
– Simple random sampling (2:13)
– Systematic sampling (2:48)
– Stratified sampling (3:30)
– Cluster sampling (4:14)
– Non-probability sampling (4:50)
– Convenience sampling (5:25)
– Quota sampling (6:08)
– Purposive sampling (6:58)
– Snowball sampling (7:46)
– Conclusion (8:23)
✏️ Trascript of the video
▶ Sampling Techniques
In statistics, population refers to the entire set of observations that are of interest to an analyst. The term is often used to contrast with a sample, which is a subset of the population.
Sampling aims to collect a representative subset of the population.
Sampling is used when observing all population data could:
1. Request excessive economic resources
2. Request more time than the one made available
3. Destroy the entire population (for example in the case of destructive tests)
To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole.
▶ Sampling Methods
There are two types of sampling methods:
Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.
Each of these sampling Methods has 4 subsections.
Probability sampling methods
1. Simple random sampling
2. Systematic sampling
3. Stratified sampling
4. Cluster sampling
Non-probability sampling methods
1. Convenience sampling
2. Quota sampling
3. Purposive sampling
4. Snowball or referral sampling
Sampling is a way to have meaningful information for the analyst without having to analyze the entire population.
Sampling saves time and money.
It is important to choose the right sampling technique for the information you intend to obtain.
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