Define sampling? Discuss two methods of probability sampling with suitable examples.

Sampling is a fundamental technique used in research and data analysis to gather information about a larger population. It involves selecting a subset, or a sample, from the larger population in order to make inferences or draw conclusions about the entire population. Sampling is an essential tool because studying an entire population can be time-consuming, expensive, and sometimes impractical. By studying a well-selected sample, researchers can make valid inferences about the population as a whole.

There are two main categories of sampling methods: probability sampling and non-probability sampling. In this discussion, we will focus on probability sampling methods, which involve selecting samples in a way that every element in the population has a known, non-zero chance of being included in the sample. Probability sampling methods ensure that the sample is representative of the population, making the inferences drawn from the sample more accurate and reliable.

Probability Sampling Methods:

1. Simple Random Sampling:

Simple random sampling is one of the most basic and straightforward probability sampling methods. In this method, each individual or element in the population has an equal chance of being selected for the sample. This ensures that the sample is representative and unbiased, as every member of the population has an equal opportunity to be included.

Example: Imagine a university with 1,000 undergraduate students. Researchers want to understand the study habits of these students. They assign each student a unique number, from 1 to 1,000. Using a random number generator, they select a sample size of 100 students. The selected students represent the sample for the study. Since every student had an equal chance of being selected, this is an example ________ __________ _______ __________ _________ __________ _____ _____ ____.
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