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Tor is a popular low-latency anonymous communication system that focuses onusability and performance: a faster network will attract more users, which inturn will improve the anonymity of everyone using the system. The standardpractice for previous research attempting to enhance Tor performance is to drawconclusions from the observed results of a single simulation for standard Torand for each research variant. But because the simulations are run in sampledTor networks, it is possible that sampling error alone could cause the observedeffects. Therefore, we call into question the practical meaning of anyconclusions that are drawn without considering the statistical significance ofthe reported results.  In this paper, we build foundations upon which we improve the Torexperimental method. First, we present a new Tor network modeling methodologythat produces more representative Tor networks as well as new and improvedexperimentation tools that run Tor simulations faster and at a larger scalethan was previously possible. We showcase these contributions by runningsimulations with 6,489 relays and 792k simultaneously active users, the largestknown Tor network simulations and the first at a network scale of 100%. Second,we present new statistical methodologies through which we: (i) show thatrunning multiple simulations in independently sampled networks is necessary inorder to produce informative results; and (ii) show how to use the results frommultiple simulations to conduct sound statistical inference. We present a casestudy using 420 simulations to demonstrate how to apply our methodologies to aconcrete set of Tor experiments and how to analyze the results.

Once is Never Enough: Foundations for Sound Statistical Inference in Tor Network Experimentation