The Null Hypothesis
In order to understand what an alternate hypothesis is, you first need to understand what the null hypothesis means. The word hypothesis means a working statement. In statistics, we’re interested in proving whether a working statement (the null hypothesis) is true or false. Usually, these working statements are things that are expected to be true — some kind of historical or existing expected value. The word “null” can be thought of as “no change”. With the null hypothesis, you get what you expect, from a historical point of view.
The alternate hypothesis will typically be the expected change in some portion of the statistical graph when compared to a null hypothesis that represents no change. When looked at conventionally it may seem like a redundant technique, but it plays an important role in the development of some Statistics practices such as the Neyman-Pearson lemma. In many cases, the alternate hypothesis will just be the negation of the null hypothesis. For example, the null hypothesis might be “There was no change in the water level this Spring,” and the alternative hypothesis would be “There was a change in the water level this Spring.”
In other cases, there might be a change in the amount of something. For example, let’s say a Gallup poll predicts an election will re-elect a president with a 5 percent majority. However, you, the researcher, has uncovered a secret grassroots campaign composed of hundreds of thousands of minorities who are going to vote the opposite way from expected.
Null hypothesis: President re-elected with 5 percent majority
Alternate hypothesis: President re-elected with 1-2 percent majority.
Although the outcome hasn’t changed (the President is still re-elected), the majority percentage has changed — which may be important to an electoral campaign.