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SpicyBoi SpicyBoi
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3 years ago
If in reality H0 is true, then designing a very powerful experiment (without changing alpha) will allow us to reject H0 more readily.
 
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If H0 is true, power is the probability of not making a Type II error.
 
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Alpha + beta = 1.
 
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A power analysis is useful when the results of an experiment fail to reach significance.
 
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High power is desirable in an experiment.
 
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Power is the probability of rejecting H0 if it is false.
 
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Other factors held constant, a one-tailed test is more powerful than a two-tailed test.
 
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Increasing N increases the probability of a Type II error.
 
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Power and beta are inversely related.
 
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In general, the sum of alpha and beta should equal 1.
 
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ShmartboiShmartboi
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3 years ago
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wrote...
3 years ago
tremendous help
wrote...
3 weeks ago
Thx
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