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Random Selection
Random selection is a precise, scientific procedure whereby each unit in a population has an equal chance of selection for inclusion in a sample. This concept underlies the premise of probability sampling and is central to probability methodologies and generalizability. Random selection eliminates sampling selection bias but introduces random error in its place. Random selection is the only valid strategy for obtaining a representative sample in research.
Random Selection and Probability Sampling
Probability theory, which emerged in response to a desire to better understand card games and gambling, is the branch of mathematics dealing with how to estimate the chance that events will occur. In probability theory, the probability of something occurring is usually expressed as the ratio between the number of ways an event can happen and the total number of things that can happen. For example, the probability of picking a heart from a deck of cards is 13/52. In other words, inferences are made about events based on knowledge of the population.
There are two types of sampling methodologies that can be used in research: probability and non-probability. Each methodology emphasizes different sample selection procedures. Probability sampling methods all use a random selection procedure to ensure that no systematic bias occurs in the selection of elements. The term random selection is commonly used in the context of experimental research, while the term random sampling is more common to survey research; however, the underlying principle is the same. In probability methodologies, each unit of the population has a known nonzero chance of being selected. Probability sampling is typically used for quantitative research and large sample sizes and is considered to be representative of reality by statistical inference.
The most simple and common example of random selection is the tossing of a perfect coin. Each toss of the coin can result in two outcomes, heads or tails, with both outcomes having an equal probability of occurrence, that is, 50%. Moreover, each event is totally independent of previous selections and is also mutually exclusive, that is, a head and a tail cannot occur at the same time.
There are several types of probability sampling methods, namely, simple random sampling, systematic sampling, stratified random sampling, and cluster sampling.
Simple Random Sampling
In simple random sampling, every element in the population has an equal chance of being selected. This method is ideal for drawing a sample but would be quite time-consuming and difficult if a large population is involved. The basic procedure involves the following: identifying the research population, enumerating each element, and devising a selection method to ensure that each element has an equal chance of selection. For example, if a class has 100 students and the teacher wanted to take 10 students on a field trip, the teacher could write each student's name on a piece of paper, place all the names in a bottle, and then randomly select 10 names. A more sophisticated alternative would be to use a random number table to select the 10 students. Using this technique, each student would be assigned a number from 1 to 100. An arbitrarily starting point on the random number table would then be selected. The teacher would then move down the columns searching for the first 10 numbers on the table that carry 1 to 100 as their first 3 digits.
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