1. Statistical Power

2. H o : Treatments A and B the same H A : Treatments A and B different

3. Points on this side, only 5% chance from distribution A. Area = 5% Critical value at alpha=0.05 Frequency A A could be control treatment B could be manipulated treatment

4. A B If null hypothesis true, A and B are identical Probability that any value of B will be not significantly different from A = 95% Probability that any value of B is significantly different than A = 5%

5. 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 What you say: Reality

6. A B If null hypothesis true, A and B are identical Probability that any value of B will be not significantly different from A = 95% Probability that any value of B is significantly different than A = 5% = likelihood of type 1 error

7. A B If null hypothesis false, two distributions are different Probability that any value of B will be not significantly different from A = beta = likelihood of type 2 error Probability that any value of B is significantly different than A = 1- beta = power

8. A B Effect size Effect size = difference in means SD

9. A B 1. Power increases as effect size increases Beta = likelihood of type 2 error Power Effect size

10. A B 2. Power increases as alpha increases Beta = likelihood of type 2 error Power

11. A B 3. Power increases as sample size increases Low n

12. A B 3. Power increases as sample size increases High n

13. Power Effect size Alpha Sample size

14. 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.

15. Example : Fox hunting in the UK (posteriori)

16. 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

17. 157 plots where the fox population monitored. Alpha = 0.05 Effect size if hunting affected fox populations: 13%

18. 157 plots where the fox population monitored. Alpha = 0.05 Effect size if hunting affected fox populations: 13% Power = 0.95 !

19. Class exercise: Means and SD of parasite load (p>0.05): Daphnia magna 5.9 ± 1 (n = 3) Daphnia pulex 4.9 ± 1 (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? Hint: see table.

20. Good options for increasing sample size: More replicates More blocks False options for increasing sample size: More “repeated measurements” Pseudoreplication