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You should use a one-tailed test: only in rare instances in which values on one side of the value stated in the null hypothesis are inconceivable. in most circumstances, because you are more likely to get a low P-value. whenever you want to analyze parameter values on both sides of the parameter value specified by the null hypothesis O whenever it is conceivable that the alternative hypothesis could fall on either side of the parameter value specified by the null hypothesis.

You should use a one-tailed test: only in rare instances in which values on one side of the value stated in the null hypothesis are inconceivable. in most circumstances, because you are more likely to get a low P-value. whenever you want to analyze parameter values on both sides of the parameter value specified by the null hypothesis O whenever it is conceivable that the alternative hypothesis could fall on either side of the parameter value specified by the null hypothesis.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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You should use a one-tailed test: only in rare instances in which values on one side of the value stated in the null hypothesis are inconceivable. in most circumstances, because you are more likely to get a low P-value. whenever you want to analyze parameter values on both sides of the parameter value specified by the null hypothesis whenever it is conceivable that the alternative hypothesis could fall on either side of the parameter value specified by the null hypothesis.
Transcribed Image Text:You should use a one-tailed test: only in rare instances in which values on one side of the value stated in the null hypothesis are inconceivable. in most circumstances, because you are more likely to get a low P-value. whenever you want to analyze parameter values on both sides of the parameter value specified by the null hypothesis whenever it is conceivable that the alternative hypothesis could fall on either side of the parameter value specified by the null hypothesis.
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