Probability sampling and simple random sampling are both techniques used in research to select a sample from a population, but they differ in their methods of selection and implementation. Simple random sampling involves selecting individuals from a population in such a way that each individual has an equal probability of being chosen. This method typically entails assigning a number to each member of the population and then using a random process, such as a random number generator, to select the desired sample size. For example, if a researcher wants to study the crime rates in a city, they might assign a number to each household and then randomly select a certain number of households to be part of the sample (Helen, B.). Probability sampling encompasses a range of techniques where each member of the population has a known probability of being selected for the sample. This includes methods such as stratified sampling, where the population is divided into distinct subgroups or strata, and then samples are randomly selected from each stratum. Another example is cluster sampling, where the population is divided into clusters or groups, and then clusters are randomly selected for inclusion in the sample. For instance, in a study on the effectiveness of different policing strategies across multiple precincts, the researcher might use cluster sampling by randomly selecting several precincts and then studying the data within those chosen precincts. In my own research, I've oft