Statistical Significance of a Simple Test Please help me with this basic question on statistics:  If a standard brick is dropped on a standard raw chicken egg from 1 meter; the egg breaks. How many times does this dropped-brick-onto-egg need to be repeated to establish statistical significance? At what point can it be stated that: "It has been proven that a brick dropped onto a raw egg will cause the egg to break" or "It has been proven to a 99% percent certainty."
(This is not a riddle or a joke. I'm trying to understand this basic concept.)
 A: Laplace dealt with similar issues when he asked "What is the probability the sun will rise tomorrow?" It seems that there are things that we can know with certainty, which a priori doesn't seem to mesh with hypothesis testing.
However, note that before you can perform a hypothesis test, you need to understand what the world would look like under the null hypothesis. If your null hypothesis is: "A standard brick cannot break a standard egg when drooped from 1 meter", then all it takes is one demonstration to the contrary to be 100% sure. A more interesting case would be if you said there is a 99.999% chance of the brick breaking the egg (as a null hypothesis) vs the probability being greater than 99.999%. This would require a very large number of trials.
But...all this assumes that there is a random relationship between the brick hitting th egg and it breaking. At the macroscopic level, this apperars to be incorrect. A physicist or mechanical engineer, if given the exact parameters of your experiment, would be able to predict with almost certianty, what will happen. In fact, violoations of their predicitons would prompt an investigation of your expreiment, not skepticism of their calculations. So, if you have  a cauasal law that is virtually certain to apply, then hypothesis testing is not an appropriate way to resolve this question....you need some actual randomness and some model of that randomness.
A: These wikipedia pages should be useful to you: https://en.wikipedia.org/wiki/Statistical_significance and https://en.wikipedia.org/wiki/P-value.  Also Kahn Academy is always good too https://www.khanacademy.org/math/probability/statistics-inferential/hypothesis-testing/v/hypothesis-testing-and-p-values (this is a great example).
Basically you (or your boss/teacher/someone) determines what "statistical significance means. Often "p-values" of 5% or 1% are chosen (95% or 99% accuracy); though in other contexts they are chosen as $n\sigma$ (often $n=3$) where $\sigma$ is the standard deviation.
