Statistical Power
Ho : Treatments A and B the same HA: Treatments A and B different
Critical value at alpha=0.05 Points on this side, only 5% chance from distribution A. Frequency A Area = 5% A could be control treatment B could be manipulated treatment
If null hypothesis true, A and B are identical Probability that any value of B is significantly different than A = 5% A B Probability that any value of B will be not significantly different from A = 95%
What you say: Decide NOT significantly different (do not reject Ho) Decide significantly different (reject Ho) Ho true (same) Type 1 error Ho false (different) Type 2 error Reality
If null hypothesis true, A and B are identical Probability that any value of B is significantly different than A = 5% = likelihood of type 1 error A B Probability that any value of B will be not significantly different from A = 95%
If null hypothesis false, two distributions are different Probability that any value of B is significantly different than A = 1- beta = power A B Probability that any value of B will be not significantly different from A = beta = likelihood of type 2 error
Effect size = difference in means SD B Effect size = difference in means SD
1. Power increases as effect size increases B Beta = likelihood of type 2 error
2. Power increases as alpha increases B Beta = likelihood of type 2 error
3. Power increases as sample size increases Low n A B
3. Power increases as sample size increases High n A B
Effect size Alpha Power Sample size
Types of power analysis: A priori: Useful for setting up a large experiment with some pilot data Posteriori: Useful for deciding how powerful your conclusion is (definitely? Or possibly). In manuscript writing, peer reviews, etc.
Example : Fox hunting in the UK (posteriori)
Hunt banned (one year only) in 2001 because of foot-and-mouth disease. Can examine whether the fox population increased in areas where it used to be hunted (in this year). Baker et al. found no effect (p=0.474, alpha=0.05, n=157), but Aebischer et al. raised questions about power. Baker et al. 2002. Nature 419: 34 Aebischer et al. 2003. Nature 423: 400
157 plots where the fox population monitored. Alpha = 0.05 Effect size if hunting affected fox populations: 13%
157 plots where the fox population monitored. Alpha = 0.05 Effect size if hunting affected fox populations: 13% Power = 0.95 !
Class exercise: Means and SD of parasite load (p>0.05): Daphnia magna 5.9 ± 2 (n = 3) Daphnia pulex 4.9 ± 2 (n = 3) (1) Did the researcher have “enough” power (>0.80)? (2) Suggest a better sample size. (3) Why is n=3 rarely adequate as a sample size?
Good options for increasing sample size: More replicates More blocks False options for increasing sample size: More “repeated measurements” Pseudoreplication