Comparative analysis of sorting algorithm performance on normally and poisson distributed integer arrays

Sorting algorithms are very important in many fields, such as computer science, engineering and technology. This paper analyses the speed of these algorithms through simulations on two types of datasets: one with a normal distribution and the other with a Poisson distribution. Datasets of varying sizes, means, and variances were generated to assess how these characteristics influence sorting algorithm performance in terms of execution time. The algorithms tested include quicksort, mergesort, heapsort, radix sort, insertion sort, and bubble sort. The results indicate that quicksort, mergesort, heapsort, and radix sort consistently performed well across both distributions, while bubble sort showed significant inefficiencies as dataset size increased. Generally, sorting on Poisson-distributed data was faster due to its discrete nature, but bubble sort remained inefficient regardless of the distribution. These findings highlight the impact of data distribution on sorting algorithm efficiency.