This preview shows pages 1–3. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.View Full Document
Unformatted text preview: Goals of experiments
•! Eliminate bias •! Reduce sampling error (increase precision and power) Design features that reduce bias
•! Controls •! Random assignment to treatments •! Blinding Controls
•! A group which is identical to the experimental treatment in all respects aside from the treatment itself. Example: placebo
•! Some illnesses, e.g. pain and depression, respond to fact of treatment, even with no pharmaceutically active ingredients •! Control: "sugar pills" Example: independent recovery
•! Patients tend to seek treatment when they feel very bad •! As a result, they often visit the doctor when they are at their worst. Improvement may be inevitable, even without treatment •! Therefore, we need a control, untreated group to compare with, if we want to measure the effects of a new therapy Random assignment averages out the effects of confounding variables Experiment: individuals are randomly assigned to treatments Blinding
•! Preventing knowledge of experimenter (or patient) of which treatment is given to whom •! Unblinded studies usually find much larger effects (sometimes threefold higher), showing the bias that results from lack of blinding Reducing sampling error
Increasing the signal to noise ratio Reducing sampling error
Increasing the signal to noise ratio
!1 1 s2 # + &. p n1 n 2 % t= Y1 ! Y2 1 1% s + # n1 n 2 &
2 p "Signal" If the "noise" is smaller, it is easier to detect a given "signal".
"Noise" Can be achieved with smaller s or larger n. Balance increases precision
SE Y ! Y
1 Blocking accounts for extraneous variation
C = Control T = Treated 2 1 1% =s + . # n1 n2 &
2 p For a given total sample size (n1+n2), the standard error is smallest when n1=n2. Variance among hospitals will not contribute to SE. Only variance within hospitals will contribute to "noise" ...
View Full Document
This note was uploaded on 04/16/2010 for the course MATHEMATIC 1231 taught by Professor Driscoll during the Spring '10 term at Clayton College of Natural Health.
- Spring '10