The media has given extensive coverage to experimental research in social sciences in the recent months following the Nobel Committee's decision to award the Economics prize to Abhijit Banerjee, Esther Duflo and Michael Kremer. The three economists work is premised on evidence from randomised controlled trials designed to isolate the effect of an intervention on an outcome or event by comparing its impact on a treatment group that gets the intervention with a control group that does not get the intervention. Testing interventions in pilot settings thus prevents the state from pursuing ineffective courses of action. However, there is a conspicuous lack of experimental work in the field of legal research in India. Rigorous RCTs are indeed difficult to carry out in legal settings, given the complexity of the legal system and the need to ensure that any such studies do not hinder people's access to justice. But there is a great opportunity to incorporate some of these methods from RCTs into legal policymaking. The Indian judicial system is plagued with problems of delay and backlog. Currently, 3.5 crore cases are pending across the country's high courts and district courts. The long-term consequence of such high pendency is an erosion of faith in the institution of the judiciary. Justice delivery is the monopoly of the state but delays and the cost of litigation have led to people approaching non-judicial bodies outside the formal court system such as khap panchayats, religious leaders and politicians for dispute resolution. The problem of judicial delay, however, stubbornly persists. The most common solution proposed using a simplistic input-output model is to increase the number of judges. This suggestion conveniently masks the deeper systemic flaws in the judicial system that cause such high pendency. Further, despite the seriousness of the issue, there has been no empirical study on the effect of increasing the number of judges on judicial pendency. Using the experimental method will allow researchers to test a causal relationship between an independent variable and possibly dependent variables. Experiments such as these will give policymakers insights into how certain interventions work at a smaller scale before deciding on large-scale implementation.