Let’s consider a hypothesis that many teachers might subscribe to: that students work better on Monday morning than they do on a Friday afternoon (IV=Day, DV=Standard of work).
Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and on a Friday afternoon and then measuring their immediate recall on the material covered in each session we would end up with the following:
The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.
The null hypothesis is, therefore, the opposite of the alternative hypothesis in that it states that there will be no change in behavior.
At this point, you might be asking why we seem so interested in the null hypothesis. Surely the alternative (or experimental) hypothesis is more important?
Well, yes it is. However, we can never 100% prove the alternative hypothesis. What we do instead is see if we can disprove, or reject, the null hypothesis.
If we reject the null hypothesis, this doesn’t really mean that our alternative hypothesis is correct – but it does provide support for the alternative / experimental hypothesis.