Hi there. In this post, I provide a short overview of sample types when it comes to data collection and statistics. A big part of data analysis, statistics, business intelligence and artificial intelligence is data collection. Data is collected for analysis and to find insights on items or people. Whatever is found from the data can be used for decision making purposes. A population refers to the selected group of interest that researchers want to know about. As populations can be really big, researchers take a sample from the population. Samples should be as large as possible but there are time constraints and budget concerns. ## Topics --- * Simple Random Sampling * Stratified Sampling * Cluster Sampling * Systematic Sampling * Convenience Sampling * Voluntary Sampling There are more sampling techniques but I cover these ones. ## Simple Random Sampling --- With simple random sampling or SRS for short, each person or object in a population has an equal chance of being selected. The selected people or items would be studied for information gain.

Image Source ## Stratified Sampling --- Stratified sampling involves separating the population into groups or strata. Then you choose a certain number of people items from each of the strata. As an example, you select ten people from male athletes at a school and then ten female athletes. This is preferable over selecting 20 random athletes where there may be an unequal amount of male and females. Another example can be stratified sampling over regions. Five representatives would be chosen from each of the thirty districts for a study.