Concept of Power of Hypothesis Testing
• Power of Hypothesis Test probability of not committing a Type II
• Effect Size: the difference between the true values and the value
from the null hypothesis.
– The true value is an alternative value of the population
parameter, assuming that the null hypothesis is false
– EFFECT SIZE= TRUE VALUE- HYPOTHESIZED VALUE
– Example: if the Ho says that the population mean is 20 and a
statistician asks “what is the probability of rejecting the null
hypothesis if the true population mean is 15?” Then the effect
size would be: 15-20= =-5
Factors that Affect Power
• Power of hypothesis test is affected by 3 factors:
1. Sample size (n)- the greater the sample size, the
greater the power
2. Significance level (α)- the higher the α, the higher
the power of the test
*If you increase the significant level, the region of
acceptance is reduced. Which means it becomes more
likely to reject the null
* This then makes it less likely to not reject the null
hypothesis when it is false, which means the less likely it is
to make a Type II error, resulting in the power of the test
to be increased.
3. “true” value of the parameter- the greater the effect
size, the greater the power of the test
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