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**gfkasjhfg** · 08-04-2012, 09:48 AM

Well, in general you set your hypothesis before you collect data.

In your example, you are technically wrong. You can only accept (fail to reject) or reject a hypothesis - rejecting a hypothesis does not make the contrary true. By rejecting the hypothesis, you are saying that your particular dataset does not support your hypothesis.

In this case, it is binary, so the opposite is always true, but don't get into the habit of thinking the choices are accept null hypothesis or accept alternative hypothesis. That could get you murdered by a pedantic.

To answer your question, there are different schools of thought on this. Purists (my professor was one these) believe that you can only run hypothesis tests once per set of data, others do not care.

Personally, I think that if you are changing your hypothesis around you are probably missing the point.

08-04-2012, 09:48 AM	#2
gfkasjhfg Join Date Oct 2005 Posts 427 Senior Member	Well, in general you set your hypothesis before you collect data. In your example, you are technically wrong. You can only accept (fail to reject) or reject a hypothesis - rejecting a hypothesis does not make the contrary true. By rejecting the hypothesis, you are saying that your particular dataset does not support your hypothesis. In this case, it is binary, so the opposite is always true, but don't get into the habit of thinking the choices are accept null hypothesis or accept alternative hypothesis. That could get you murdered by a pedantic. To answer your question, there are different schools of thought on this. Purists (my professor was one these) believe that you can only run hypothesis tests once per set of data, others do not care. Personally, I think that if you are changing your hypothesis around you are probably missing the point.
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