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Interpreting P-value (Significance tests)
Assuming (context of Ho), there is a (percent) probability of getting a sample (proportion/mean) of (p/mu) or (more/less) purely by chance.
Conclusion (Significance tests)
Because (p-value) is (less than/greater than) (alpha), we (reject/fail to reject) the Ho. We (have/do not have) convincing evidence of (Ho in context).
Interpreting Power
If the true (proportion/difference in proportions) of (context) is (sample proportion), there is a (Power) probability of correctly rejecting the null of (Ho).
p-value is less than significance level…
significant → reject null
p-value is greater than significance level…
not significant → fail to reject the null
6.8 1) state:
6.8 2) plan:
1 sample z-test conditions: 1) rand. sample, 2) 10%, 3) large counts
6.8 3) do:
z-score (standardized test statistic) & p-value w/ graph
6.8 4) conclude:
Because (p-value) is (less than/greater than) (alpha), we (reject/fail to reject) the Ho. We (have/do not have) convincing evidence of (Ho in context).
finding p-value
z-score formula: z=p-hat-p/(root p(1-p)/n) use chart to find p-value
2-sided test
Ha is not equal to p; multiply p-value by z to get 2-sided p-value
6.11 Hypotheses
H0: p1-p2=0 (p1=p2) Ha: p1-p2 </>, not equal to zero → p1<p2, p1>p2, p1 not equal to p2
sample proportion
p-hat = (x1+x2)/(n1+n2) = total successes/total sampled
conditions
random for both samples or random assignment; 10% for both samples or state not necessary due to experiment
Type I Error
Truth: H0 true, Conclusion: Reject H0 P(Type I)=Alpha (Significance Level)
Type II Error
Truth: Ha true, H0 false, Conclusion: Fail to reject H0
Consequences…
Health consequences always considered worse
Power
Truth: Ha true, Conclusion: Reject H0 P(Reject H0 I Ha is true) P(Power)=1-P(Type II)
Increase Power by:
increasing n, increasing alpha, increasing distance btwn H0 & Ha
Note 
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