Power:
The probability that you would
identify a true effect of the
independent variable.
Type Two error:
Labeling an outcome as related to
random events when it actually is
because of the independent variable.
Type Two error:
Accepting the null hypothesis when it is
false.
 error
Where  represents the probability that
this type of error would occur.
Ho :  = 50
H1:  > 50
 = .05
50
o
0
1


Power = 1 - 
0
1


Three things can effect power.
1) The level of significance
 gets smaller

gets bigger
2) Increase the sample size (N)
Larger N - smaller standard error
Therefore less overlap of the distributions
3) Increase the distance from 0 to  1
0
1

