The null hypothesis is a statement about the value of a population parameter developed for the purpose of testing numerical evidence. (Lind, et al., 2011)
Null hypothesis basically means there is no change between the mean and a number. It is developed to test whether or not to reject or fail to reject the hypothesis. It is not rejected unless sample data provides convincing evidence that it should be rejected. If this is rejected, it is not to say that the null hypothesis is proven true, but that it only is not proven untrue. To prove it is true, the whole population would have to be tested, and for many populations, that would be impossible.
The alternate hypothesis is a statement that is accepted if the sample data provide sufficient evidence that the null hypothesis is false. (Lind, et al., 2011)