An infinite number of tests yes, it is like trying to find the best possible predictor of a source. No statistical test can prove that a source has full entropy and uniformly distributed, only disprove it. These tests are too often misused and are usually not very useful since they tell us nothing about how much entropy might be in the sample, and entropy sources are always biased anyway and fail the tests, and if you condition them then they will always pass the test even if not random at all. A better method is to develop a stochastic model of the entropy source and attempt to estimate bounds on the entropy, which is still generally not feasible. The statistical tests can then be used as a sanity check to verify your entropy estimates. The RNG needs a conditioning phase, or entropy extractor to transform the output into a distribution that is indistinguishable from uniform as is needed in cryptographic applications and you should always include this stage because virtually no physical entropy source has a uniform distribution. The best thing to do is to try to gather as much entropy as you can from sources and gather maybe 10x what you think you need and then put it through an entropy extractor like a cryptographic hash function to generate a PRNG seed then use the PRNG.