What will cause Systematic Random Sampling to be biased?

Systematic Random Sampling involves selecting samples based on a fixed interval from a randomly chosen starting point. When the starting point is not randomly selected, it introduces bias into the sampling process. This is because the fixed interval may coincide with inherent patterns within the population, leading to overrepresentation or underrepresentation of certain groups.

For instance, if you are sampling people in a line and start at a particular position that happens to fall on a specific characteristic (e.g., all participants are wearing the same color), the sample may not adequately reflect the diversity of the entire population. This non-random starting point can systematically skew the results, making them unrepresentative of the population.

In contrast, if the starting point is randomly selected, the sample is more likely to be representative since it mitigates the risk of coincidental patterns affecting the selection process. Therefore, the way the starting point is determined is crucial to avoiding bias in systematic sampling.