What causes Systematic Random Sampling to be a biased sampling method?

Systematic Random Sampling can lead to bias if there is a pattern present in the population from which the sample is drawn. This method involves selecting every kth individual from a list or a sequence. If the population has a systematic arrangement or periodic patterns, the sampling could end up favoring certain characteristics or segments over others, which compromises the randomness and representativeness of the sample.

For instance, if individuals in a population are organized in a way where every kth individual shares similar traits due to an underlying order (e.g., they are part of different groups or have been affected by some cyclical factors), this can skew the results and result in biased outcomes. In contrast, random selection of the sample, inclusion of diverse demographics, or the use of stratified methods are strategies intended to enhance the representativeness of the sample, thus reducing bias rather than contributing to it.