For benchmarking SP query and comparing time distributions, the Friedman test with post-hoc Nemenyi test was used to determine if there is any significant difference amongst query time distributions. The null hypothesis was formulated as a pairwise comparison of query time distributions are not significantly different, with a predetermined significance level of p = 0.05. Due to query time execution data being skewed we required a non-parametric test such as Friedman for significance testing. Additionally, due to correlations between synthetic routes amongst various Neo4j database configurations we were unable to assume measurement independence. The Nemenyi post-hoc test was developed to account for a familywise error and the number of groups being compared is already accounted for in the test, thus Nemenyi doesn't require a p-value adjustment. Scipy stats package was used for computing Nemenyi post-hoc tests and only returns 3 digits . In addition to p-values, the effect size was computed using eta-squared . If eta-squared was less than 0.01, then the significance test was automatically considered to be non-significant.