For a sample to be statistically valid, what must be ensured?

For a sample to be statistically valid, it is essential that the samples are selected at random with proper representation. This approach ensures that every member of the population has an equal chance of being included in the sample, which minimizes bias and enhances the reliability of the findings. Random sampling allows for the results to be generalizable to the entire population, making it possible to draw conclusions that are more likely to reflect the true characteristics or behaviors of that population.

Using proper representation in the sampling process ensures that all segments of the population are adequately represented, which is critical in health care fraud investigations where different demographics or types of service may present varying levels of risk. This statistical validity is a cornerstone of effective data analysis, particularly in contexts that rely on empirical evidence to make decisions or formulate policies.

The other choices do not promote statistical validity in the same way. Using convenience sampling can introduce significant bias, limiting the generalizability of the results. Limiting samples to previous audits or only focusing on high-risk areas can further distort the results and may overlook other crucial factors in the population that need assessment.