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Sampling distribution in statistics works by providing the probability distribution of a statistic based on a random sample. An example of this is figuring out the probability of running out of water on a camping trip.
Bionomial
This depends on what information you have. If you know the success probability and the total number of observations, you can use the given formulas. Most of the time, this is the case. If you have data or experience which allow you to estimate the parameters, it may sometimes happen that you work like this. This mostly happens when n is very large and p very small which results in an approximation with the Poisson distribution.
You can calculate a result that is somehow related to the mean, based on the data available. Provided that you can work out its distribution under the null hypothesis against appropriate alternatives, you have a test statistic.
Area to the left of z = -1.72 = area to the right of z = 1.72 That is ALL the "working" that you will be able to show - unless you are into some serious high level mathematics. Most school teachers and many university lecturers will not be able to integrate the standard normal distribution: they will look it up in tables. (I have an MSc in Mathematical Statistics and I could do it but not without difficulty). Pr(z < -1.72) = 0.042716