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There are basically two types of sampling:• Probability Sampling: The sample has a known probability of being selected.• Non-probability Sampling: The sample does not have a known probability of being selected, as in convenience or voluntary response surveys or surveys based on the subjective judgment of the researcher.The latter can often be viewed as an inferior alternative to probability sampling techniques and are not used to make generalizations (i.e., statistical inferences).That being said, we focus on the first one, which eliminates possible conscious or inherent bias in those conducting the study. These methods are considered representatives of the population as a whole and are used to make generalizations (i.e., statistical inferences).These are the basic probability sampling types:Simple random samplingAll possible samples of n objects from the population are equally likely to be selected to be in the sample. The most common real life equivalent would be a lottery or sweepstake.Researchers can create a simple random sample with a lottery, where each member of the population is assigned a number, after which these are selected at random. For larger size populations, this method can be quite time consuming. Selecting a random sample from a large population usually requires a computer generating pseudo-random numbers and a process similar to the lottery is used; only the number assignments and subsequent selections are performed by computers instead of humans.Stratified samplingThe population is divided into groups, called strata, based on some pre-defined characteristic. Then, within each group, a simple random sample is selected. As an example, suppose we conduct a survey and we divide the population into groups based on geography - north, east, south, and west. Then, within each group, we randomly select respondentsCluster samplingIn this method, every member of the population is assigned to one, and only one, group, called a cluster. A sample of clusters is chosen, using a simple random sample. Only individuals within sampled clusters are surveyed.To proceed, the population is divided first into N groups or clusters. Then n clusters are randomly selected to include in the sample. Each element of the population can be assigned to only one cluster. This method is better used when the population is concentrated in "natural" clusters (city blocks, schools, hospitals, etc.).For example, to conduct personal interviews of emergency room doctors, it might make sense to randomly select a sample of hospitals and then interview all of the emergency room doctors at that hospital. Using cluster sampling, the interviewer could conduct many interviews in a single day at a single hospital. Simple random sampling, in contrast, might require the interviewer to spend all day traveling to conduct a single interview at a single hospital and so on to cover as many hospitals as needed for the sampling.The difference between cluster sampling and stratified sampling is that with stratified sampling, the sample includes elements from each stratum and with cluster sampling, the sample includes elements only from sampled clusters.Systematic random samplingWith this method, we create a list of every member of the population. From this list, we randomly select the first sample element from the first k elements . This interval (k), called the sampling interval, is calculated by dividing the population size by the desired sample size. Thereafter, we select every kth element on the list.Despite the sample population being selected in advance, systematic sampling is still thought of as being random if the periodic interval k is determined beforehand and the starting point is random. However, this method is different from simple random sampling since every possible sample of n elements is not equally likely.For example, a researcher has a population total of 100 individuals and need 12 subjects. He first picks his interval as 8 (k = 100/12), then choose his starting number at random form 1 to 8, lets say 5 . The members of his sample will be individuals 5, 13, 21, 29, 37, 45, 53, 61, 69, 77, 85, 93.Multistage samplingWith this method, we select a sample by using combinations of different sampling methods. For example, in Stage 1, we might use cluster sampling to choose clusters from a population. Then, in Stage 2, we might use simple random sampling to select a subset of elements from each chosen cluster for the final sample.Here’s a full explanation of sampling: Sampling (statistics) - Wikipedia