"The Null Hypothesis" is a theoretical alternate universe, used to measure statistical significance. The standard deviation and mean are quantitative measures, used to bootstrap or simulate the probability distribution of a given facet of your alternate universe.

If the piece of evidence you encountered, were it found your alternate universe, would be super super super super super super super *duper* unlikely, then your alternate universe probably isn't reality, which means your null hypothesis should probably be rejected.

Bootstrapping the alternate universe depends upon the probability distribution that you need to simulate your metric, which in turn depends upon whether you are dealing with so-called "frequentist" or Bayesian probability.

It also depends upon how risk adverse you want to be, when setting your probability threshold for what counts as "super duper" unlikely. The rest is mechanics, coding, and technicality.

There it is. I explained hypothesis testing in 150 words.