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
The alternate hypothesis is just an alternative to the null. For example, if your null is “I’m going to win up to $1000″ then your alternate is “I’m going to win more than $1000.” Basically, you’re looking at whether there’s enough change (with the alternate hypothesis) to be able to reject the null hypothesis.
In many cases, the alternate hypothesis will just be the opposite 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.
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