1. Statistical Power

2. Statistical Power
Definition: The probability that will reject a false null hypothesis

3. Decision process Population Decision H0 is true H0 is false Keep H0
Correct
(1-a)
Type II error (b)
Reject H0
Type I error (a)
(1-b)
Power

4. Statistical inference
m = 72
(1-b) b
m = 69

5. Statistical inference
m = 72
m = 70 b
(1-b)

6. Statistical inference
m = 72
m = 64 b
(1-b)

7. Effect size (One group)
Definition : It is the difference between the null hypothesis and the alternative hypothesis
DI = 0.2 (small effect)
DI = 0.5 (medium effect)
DI = 0.8 (large effect)
It gives us an idea of the magnitude of the difference that we want to detect. (Treatment effect)

8. Effect size (Two groups)

9. Effect size and power Example:
To know the power we need to use a software like G*power.

10. Effect size and power

11. Factors influencing the power
Signification level
The magnitude of the treatment effect
The variability within the population
Sample size

12. Factors influencing the power
Signification level
The magnitude of the treatment effect
The variability within the population
Sample size
The more a increases the more the power increases.

13. Factors influencing the power
Signification level
The magnitude of the treatment effect
The variability within the population
Sample size
The higher the effect of treatment is the higher the power will be

14. Factors influencing the power
Signification level
The magnitude of the treatment effect
The variability within the population
Sample size
The less variability in the population the higher the power will be

15. Factors influencing the power
Signification level
The magnitude of the treatment effect
The variability within the population
Sample size
If n increases the power will increases.

16. Sample size estimation
Type I and II errors
Effect size