Cautions Sec. 2.4, continued.
Lurking variable: has an important
effect, but not one of the variables studied.
Meatloaf shrinkage vs. placement
in oven? (cooking thermometer/not had greatest influence)
Time sequence of observations
a common one. (Learning, tiring, aging)
The trouble with lurking
variables is that by definition you don't know they're there. Look
behind every tree.
Association does not imply causation
Manatees:
Year
boat registrations
kills
If you didn't know boat registrations, would you believe that "year" was
the cause of "kills"?
(Are all boats actually registered? Possible lurking variable= unregistered
boats.)
Direction? Rooster causes sun to rise by
crowing?
Both variables "caused" by a lurking variable?
Baby rats whose mothers licked and groomed them
more grew up to be more exploratory, social, less timid.
Cause? Effect? How to tell?
Establishing that x "causes" y: difficult:
Best: Do an experiment
in which we change x, keep lurking variables under control. (Sec. 3.2)
Rats.
Otherwise: Strong
association. Consistent over many studies. Higher x-->stronger y.
X precedes y in time. A plausible mechanism exists (parallel
studies?)
Generalize rat grooming to humans?
E.g. hydrogenated oils
--> heart disease?
= = = = = = = = = = = = = = = = = = = = = = =
= = = = = = = = = = = = =
Chapters 1 and 2 have covered analyzing data
that was given to us--what it said about itself.
Informally, develop guesses,
suspicions, hypotheses about the world the data came from.
Ch. 3: Producing Data:
Aim: create data sets that
will allow us to make inferences to a larger world than just the
data we have.
Observational
Study: Observes individuals, measures variables, does not
influence the responses. (3.1)
Take Sample from a population, examine it, hope it's representative
so we can infer population is like sample.
(Not very useful for cause-and-effect--see above)
Experiment:
Imposes
treatment
on individuals, to see how the treatment
influences the response.
(3.2)
Best for cause-and-effect.
Confounding: Two variables (explanatory
or lurking) are confounded when you can't sort out their effects
on a response variable.
--Used to be: coffee drinking and smoking--most
people did both, or neither...
--Fisher thought health effects of cigarettes
were probably confounded with personality characteristics predisposing
both to cigarette addiction and heart disease, cancer.
HW Day16 Reread sec. 2.4. We'll skip 2.5 for now; Read Chapter 3, thru p.170 for this hw, rest of 3.1 for next.
| Hand in: Sec.
Sec. 2.4 p. 133 2.55 tv watching & grades 2.56 economists&pay 2.64 herbal tea = = = = = = = = = = Ch.3 Intro: p. 167, 3.1, 3.2, 3.3 exp, obs |
Read, to discuss
Sec. 2.4 p.136 2.57 firefighters, 2.58 self-esteem p. 138 2.61 shoe size/reading 2.66 Education/income = = = = = = = = = = Ch.3 Intro: p. 170, 3.5 pop, samp... p.182, 3.17 obsn/exp 3.18 novel--pop, samp. |
Optional
Sec. 2.4 p. 137 2.59 size of hospital = = = = = = = = = = Ch.3 Intro: |
| Sievers home | Math151-Sp01/Day16.htm | 3/5/01 |