Random sample is a type of sampling technique used in a wide variety of fields, including computing, in order to obtain a population sample that is representative of the larger population. Random sampling is one of the most frequently used data collection techniques, as it often provides a high amount of accuracy and reliability.
Random sampling is a process that is designed to ensure a fair and representative sample of a population. This is accomplished by assigning individuals to a sample at random, and can be used in a wide variety of situations. In computing, random sampling is used to ensure that a population is sampled fairly and accurately, and that data is collected accurately and efficiently.
Random sampling can be used to create samples for research, surveys, or other data analysis projects. By randomly selecting individuals from a population, a researcher can be assured that the sample taken is a fair and accurate representation of the larger population. The selection process for random samples is based on probability, so that the chances of any one individual being selected as part of the sample is equal to the chances of any other.
In computing, random sampling has several advantages. Random samples offer the most accurate representation of the population, as they are not influenced by any existing bias. Additionally, random samples are often the most cost-effective way to collect data, as the process does not require specialized skills, time, or resources.
Random samples are also relatively easy to analyze, as the data collected is typically of a higher quality than data collected through other means, such as convenience sampling. As a result, random samples are an important tool for computing professionals and researchers alike.
