It is not often that a comedian gives an astrophysicist goose bumps when discussing the laws of physics. But comic Chuck Nice managed to do just that in a recent episode of the podcast StarTalk. The show's host Neil deGrasse Tyson had just explained the simulation argument--the idea that we could be virtual beings living in a computer simulation. If so, the simulation would most likely create perceptions of reality on demand rather than simulate all of reality all the time--much like a video game optimized to render only the parts of a scene visible to a player. "Maybe that's why we can't travel faster than the speed of light, because if we could, we'd be able to get to another galaxy," said Nice, the show's co-host, prompting Tyson to gleefully interrupt.
Hypothesis Testing, as such an important statistical technique applied widely in A/B testing for various business cases, has been relatively confusing to many people at the same time. This article aims to summarize the concept of a few key elements of hypothesis testing as well as how they impact the test results. The story starts from hypothesis. When we want to know any characteristics about a population like the form of distribution, the parameter of interest(mean, variance etc.), we make an assumption about it, which is called the hypothesis of population. Then we pull samples from population, and test whether the sample results make sense given the assumption.