Random sample (also called a random subset) is a subset of a data set or population in which each member of the subset is selected by chance. In statistics, random sample is a subset of data taken from a larger population that accurately represents the characteristics of the whole population. By randomly selecting members of the population, without bias, researchers can draw useful conclusions about the population as a whole.

For example, a random sample of computer users’ ages could be used to determine the average age range of computer users in a particular area. Rather than producing one statistic from a single data point, a random sample could produce meaningful results by drawing from a larger number of individuals.

Random sample is a useful technique in many areas of computer science, programming, and cybersecurity. Network security analysts often use random sampling techniques to determine the prevalence of malware on a network. Selecting random samples of files and running internet security scans on them can be a useful tool for determining the overall security of a system and identifying potential points of vulnerability.

In computer programming, random sample is often used for debugging and QA testing. Selecting a random sample of data points or executing a random sample of test cases can help to increase the coverage of a given test suite, ensuring more complete testing.

Random sampling can also be used as a tool for data compression. By taking a sample from a larger set of data points, such as an image file, the file can be compressed more efficiently by removing redundant data points, maintaining the overall shape and content of the original.

Random sampling is a powerful and versatile tool important to many areas of computer science, programming, and cybersecurity. By selecting a subset of data that accurately represents a larger population, researchers and programmers can draw meaningful and accurate conclusions about the characteristics of the larger data set.

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