  Medical and social sciences demand sampling techniques which are robust,
reliable, replicable and have the least dissimilarity between the samples
obtained. Majority of the applications of sampling use randomized sampling,
albeit with stratification where applicable. The randomized technique is not
consistent, and may provide different samples each time, and the different
samples themselves may not be similar to each other. In this paper, we
introduce a novel non-statistical no-replacement sampling technique called
Wobbly Center Algorithm, which relies on building clusters iteratively based on
maximizing the heterogeneity inside each cluster. The algorithm works on the
principle of stepwise building of clusters by finding the points with the
maximal distance from the cluster center. The obtained results are validated
statistically using Analysis of Variance tests by comparing the samples
obtained to check if they are representative of each other. The obtained
results generated from running the Wobbly Center algorithm on benchmark
datasets when compared against other sampling algorithms indicate the
superiority of the Wobbly Center Algorithm.
