The research hypothesis typically states a difference or relationship in the expected direction. A null hypothesis states that no difference or relationship exists. The null hypothesis is preferred when applying statistical tests. You can never prove your hypothesis, only disprove it. Hypothesis testing is a process of disproving or rejecting, and the null hypothesis is best suited for this purpose.
The initial step in hypothesis testing then is to establish a null hypothesis. For instance, the null hypothesis for our example can be stated as follows:
No significant difference exists between the mean mathematics scores of ninth grade students who receive computer mathematics instruction and ninth grade students who receive traditional mathematics instruction.
After formulating the null hypothesis, the researcher carries out a test of statistical significance to determine whether the null hypothesis can be rejected (i.e., whether there is a true or real difference between the groups). This test enables us to make statements of the type:
If the null hypothesis is correct, we would find this large a difference between sample means only once in a hundred experiments (p < .01). Because we have found this large a difference, the null hypothesis quite probably is false. Therefore, we will reject the null hypothesis and conclude that the difference between sample means reflects a true difference between population means.