What could be a consequence of non-random sampling?

Non-random sampling often leads to a situation where the selected sample does not accurately reflect the diversity and characteristics of the entire population. When a sample is taken without randomization, certain groups may be overrepresented or underrepresented, resulting in biased outcomes. This bias can influence the validity of generalizations made about the population based on the sample data. For example, if a survey is conducted only among a specific demographic group, the findings may not apply to the broader population, leading to inaccurate conclusions.

In contrast, random sampling is designed to give all individuals an equal chance of being selected, which helps ensure that the sample represents the population more reliably. Therefore, the inability to make accurate statements about the broader population due to biases introduced through non-random sampling is a critical concern in research methodology.