Simple random sampling (also referred to as random sampling) is the purest and the most straightforward probability sampling strategy.
It is also the most popular method for choosing a sample among population for a wide range of purposes.
So, the researcher would need to narrow down the population and build a sample to collect data.
This sample might be a group of coal workers in one city.
Other variations of random sampling include the following: My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach contains a detailed, yet simple explanation of sampling methods. (2011) “Research Methods for the Behavioural Sciences” Cengage Learning p.146  Saunders, M., Lewis, P.
The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. In these cases sampling may be the only way to obtain accurate information about population characteristics.Researchers can use sample statistics to make inferences about population parameters because the laws of probability show that under specified conditions, sample statistics are unbiased estimators of population parameters.The three main types of quantitative sampling are: Sampling yields significant research result.However, with the differences that can be present between a population and a sample, sample errors can occur.Ideally, the sample size of more than a few hundred is required in order to be able to apply simple random sampling in an appropriate manner. It can be argued that simple random sampling is easy to understand in theory, but difficult to perform in practice.This is because working with a large sample size is not easy and it can be a challenge to get a realistic sampling frame.Sample-based estimates are called sample statistics, while the corresponding population values are called population parameters.The most common reason for sampling is to obtain information about population parameters more cheaply and quickly than would be possible by using a complete census of a population.Illustration of the importance of sampling: A researcher might want to study the adverse health effects associated with working in a coal mine.However, it would be impossible to study a large population of coal workers.