What happens if a sample is not randomly selected?

When a sample is not randomly selected, it may not accurately represent the population because it can introduce bias. Random selection is crucial in research as it ensures that every individual in the population has an equal chance of being included in the sample. When this process is bypassed, certain groups or characteristics within the population might be overrepresented or underrepresented, leading to skewed results. This lack of representation can affect the generalizability of the findings, making it challenging to draw valid conclusions about the larger population based on the sample data.

The other options do not correctly reflect the implications of a non-random sample. For instance, collecting data can still be possible, but the findings may not be reliable or valid. High validity is more likely achieved through proper sampling methods, not through a biased selection process. Furthermore, a non-random sample may inadvertently increase the diversity of responses depending on how the selection was made, rather than minimizing it.