Math 151 , Fall 2008 Monday Day 22, Oct. 20 Hit reload....After class.

(SAMPLING) BIAS:  The design of a study is biased if it systematically favors certain outcomes.  
Non-probability samples (sampling badly): Voluntary response sample , Convenience sample

Do something to:
    "Experimental Units" = "Subjects" = individuals.
Treatment:  Specific experimental condition we impose on one or more subjects.
Factor: Explanatory Variable we manipulate.
      There will be Specific values of a factor that we set. (Sometimes called "levels")
Response variable(s)  Results that we measure.
    E.g. Corn planting (HW day 15(?), p. 110, 4.28)  1 factor = planting rate.  5 different values (levels). 16 individuals (plots of ground). Response:  yield per acre.

E.g. 2 headache medications, in combination?
A two-factor experiment, each with 3 values (levels). 9 possible treatments.
    Factor A: Aspirin: values: None, 500 mg, 1000 mg
    Factor B: Caffeine: values: None, 50 mg, 100 mg
Response variable: reported pain relief

			Aspirin
		None	500 mg	1000 mg
	None	Treatment 1	Treatment 2	Treatment 3
Caffeine	50 mg	Treatment 4	Treatment 5	Treatment 6
	100 mg	Treatment 7	Treatment 8	Treatment 9

 Lurking variables:  Control--how?  Nothing except experimental treatment should differentially affect response.
    Compare responses under several treatments, look at differences.
Placebo effect: a positive response to a "sham" medical treatment--if you believe it will work, it very likely will. (Tinkerbell?)
    A medical treatment must be shown to be better than a placebo (at least) to be approved by the FDA.
               placebo="I shall please" (Latin)
To control for the placebo effect, All treatments should "look alike".  Treatment 1 above should be a pill with no medicine--a "placebo".  (Some experiments even try to duplicate side effects of actual medication.)

"Control group" Group that gets the "baseline"--"null"-- "none" or "placebo" value of the factor.  Should be "just like" the group(s) that get the "treatment" ("real" values of the factor).  So Treatment 1 above will go to the "control group", the other 8 will go to "experimental" or "treatment groups."
Murky language here:  "Experimental vs. control" or "Treatment vs. control" is different usage from "Treatments", one of which is the "control"="none"/"placebo".
     *Sometimes the Control  is the current "best practice" treatment, rather than none.

Sometimes (especially in bio, physics, chem experiments)  there is no "control group" --no baseline--just a sequence of different values (like corn planting experiment.)   Moore says "uncontrolled" --which doesn't mean "out of control" :-)
  In these environments also, we make everything else "the same" to try to eliminate confounding/lurking variable effects.

.Start about HERE Wed..
How to get groups "just like" one another?  Randomize who goes into which group.  (Usually our batch of  experimental units is not a random sample from the population of all individuals--volunteers, etc.)
Randomized comparative experiment : Diagrams of design, Moore pp. 218-19: shows where randomizing happens, how many to each treatment, what the treatments are.
Completely randomized: all exp. units allocated at random among the treatments.

E.g. does acupuncture work for Backache??  Response: report of symptoms.
    One factor, 3 values:  None (music??(but might work...!). "Usual treatment"), Acupuncture (wrong places), Acupuncture (right places). 3 treatments.
            (control(s)?)
      30 subjects with Backache:  Randomize, 10 each treatment.  Administer treatments.  Compare symptoms. (Do diagram)

(I'm not making this up.... Backache(1)  (2) Sept. 2007)

"In a study of 1,162 adults with chronic lower back pain, 48 percent of those in a group who underwent between 10 and 15 treatments with traditional Chinese "verum" acupuncture reported at least one-third less pain and an improvement in functional ability, with lasting benefits.  ...
That compared to 27 percent of those reporting relief in the group undergoing drug and exercise therapy.

A third group of patients underwent so-called sham acupuncture, where needles are inserted randomly and less deeply around the painful area while avoiding the medians. Of these, 44 percent reported relief from their back pain -- more patients than conventional therapy and only slightly fewer than traditional acupuncture."

Picking groups with random number table: Table B. Pick "sample" of size 10 from the 30 for first treatment group.  Pick another "sample" of size 10 for 2nd treatment group, from the remainder.  The 10 remaining get the 3rd treatment. 
Easier with  Simple Random Sample Applet.  Enter total number of subjects in "Population size", enter size of first group in "sample size", hit Reset, then Sample.  Write down the numbers for this sample, it's group 1.  Hit Sample again (DON'T reset) to choose the second group.  Write down the numbers, continue...
Why 10 each, not just 1 each?  Use enough subjects for each treatment so that you can  "average out" (and measure) chance variation in the subjects. 

Principles of designing an experiment (p. 221) See above

• Control effects of lurking and confounding variables, by comparing treatments, where values of explanatory variables=factors are controlled, and all other conditions are controlled to be the same.
• Randomize the assignment of individuals to treatments: reduces possibility of confounding characteristics of individuals with the treatments.
• Use enough subjects  for each treatment to reduce the chance of unrepresentative results. (see above)