How is a 'random sample' defined in data analysis?

A 'random sample' in data analysis is defined as a representative subset randomly taken from the larger population. This method of sampling ensures that every individual or observation in the population has an equal chance of being selected. By using random sampling, researchers can minimize biases that may occur if certain types of data points are overrepresented or underrepresented due to selection criteria.

The strength of random sampling lies in its ability to produce results that can be generalized to the entire population. This is crucial for statistical inference, as it allows conclusions drawn from the sample to be applied back to the larger group with a certain level of confidence.

Using a representative subset enables the analysis to reflect the characteristics of the population as a whole, thus providing more accurate and reliable insights. This contrasts with other sampling methods that may introduce biases or fail to accurately represent the population, ultimately affecting the quality and validity of the analysis.