Hypothesis testing begins with an assumption called a Hypothesis, that we make about a population parameter. A hypothesis is a supposition made as a basis for reasoning. The conventional approach to hypothesis testing is not to construct a single hypothesis about the population parameter but rather to set up two different hypothesis. So that of one hypothesis is accepted, the other is rejected and vice versa.
A hypothesis of no difference is called null hypothesis and is usually denoted by H0 “Null hypothesis is the hypothesis” which is tested for possible rejection under the assumption that it is true “ by Prof. R.A. Fisher. It is very useful tool in test of significance.
For example: If we want to find out whether the special classes (for Hr. Sec. Students) after school hours has benefited the students or not. We shall set up a null hypothesis that “H0: special classes after school hours has not benefited the students”.
Any hypothesis, which is complementary to the null hypothesis, is called an alternative hypothesis, usually denoted by H1, For example, if we want to test the null hypothesis that the population has a specified mean μ0 (say),
i.e., : Step 1: null hypothesis H0: μ = μ0