When asked to define "science", most people refer to the facts of science, but most scientists would mention the process of science: Science is a process of designing experiments that allow us to get results and draw conclusions from them. What I've tried to do in this course is to integrate some of the facts of physiology with a description of some of the experiments that led to our acceptance of these facts.

The first assignment asked you to think a bit about the design of an experiment. How would you find out whether people from different countries have different adaptations to temperature? A beginning student will say "See if they sweat more". But with experience, you begin to understand the many variables that must be considered. You would need to decide whether to study volume of sweat produced, or number of functional sweat glands, or time it takes till onset of sweating. You would need to decide how long to let the experiment continue; what time of day to do it; what the temperature of the room should be, etc. The experiment you would finally do would be only one of the many possible experiments that could be done to answer this question.

The design of the experiment is important, because it will determine how limited are the conclusions you can draw from the results. For example, a scientist might find the same volume of sweat produced by Green and Blue people in 80 degree weather, and conclude that there are no differences between them. But perhaps they do differ at 90 degrees. Or perhaps they produce the same volume of sweat, but use different numbers of sweat glands to do so. Or one does so faster.

Textbooks, newspapers and magazines usually give just a superficial description of experiments, and then tell you the conclusions that were drawn. To decide whether to accept these conclusions, you need to know more about how the experiment was designed. You came up with some of these sorts of questions in the first assignment. We discussed other such questions throughout the semester. What I've done below is summarize some of these questions, with examples to remind you of when we mentioned them.

THE SAMPLE

How was the sample selected? Does it reflect the population of interest?

• Framingham study - Are those who volunteer representative of all population?
• Political polls - Telephone sample - who's home? - elderly, moms w/kids, unemployed. Not random sample of all voters.

Is sample size big enough?

• RU486 was 100% successful in initial studies...till they increased sample size. Large sample had lower rate.

Is appropriate control used?

• Castrated male with ovary vs. ovariectomized female with testis - missing best control.
• Selye injected putative hormone vs. saline vehicle

Are controls and exptl subjects matched on all variables?

• Heart disease in Japanese: San Francisco vs. Nagasaki. But immigrants may be different.

Is sample influenced by place where it's taken?

• Smokers indoors or outdoors; WBC's in blood vs. tissues
• Sample of substance in urine not necessarily same as in blood.
• Sample in blood doesn't necessarily reflect what's in brain, if can't penetrate BBB.

Is sample influenced by when it's taken?

• Some substances may have been there, but disintegrate quickly (NO, PGs)
• Many parameters different during day vs. night. (GH high at night during puberty)
• Many hormones are released in pulsatile fashion; concentration varies in periodic fashion

THE METHODS

Does the technique that's used measure what you really want to know?

• RIA vs. bioassay
• Immune system studies in vivo vs. in vitro; cell number vs. cell function
• Histology vs. ultrasound for determining side of last ovulation.

What parameter of a biologically-active substance was measured?

• Easier to measure in urine or saliva, but don't necessarily reflect what's in blood
• Measure blood levels of some substance -- What does a change reflect? Change in synthesis? Degradation? How much in an inactive form, or bound to protein? How much has left the blood for interstitial spaces?
• Measure biological response to hormone -- What does a change reflect? Change in amount of hormone? Or in number of receptors?

What endpoint was used?

• Want to see if some diet affects CVD death, but instead of death as endpoint, use an endpoint that's faster to measure (% who recover from heart attack; # who get heart attack; degree of artery narrowing; blood levels of cholesterol).
• Want to see if cigarette smoking affects disability from osteoporosis. Endpoint might be number of broken bones; time takes for break to heal; bone density
• Want to see if stress affects occurrence of disease. Endpoint might be occurrence of disease...or decrease in immune response....or increase in stress hormones.
• Is the endpoint selected meaningful with respect to what we really want to know?

How long does study last?

• Cross-sectional vs. longitudinal
• Could be change found is short-term, would disappear in the long run.
• Could be no effect in short-term study, but would see one if looked for longer (stress)

INTERPRETATION

Correlation doesn't imply causation.

• Shoe size of children correlates with spelling ability, but that doesn't mean we spell with our feet; rather, both are correlated with age.

Effect may be statistically significant, but is it also biologically significant?

• Visual acuity between males and females

Have conclusions been drawn beyond what the data supports?

• Dietary fat didn't correlate with CVD, but conclusion applied only to narrow range of fat.
• Results found in animals, but don't necessarily apply to humans.

Does the situation studied reflect real-life scenario for this species?

• Dietary fat and atherosclerosis in rabbits...who don't naturally eat much fat.

FINALLY:

Every experiment is a compromise between an ideal design and what we can practically do. As we saw in the discussion of diet and heart disease, there are questions that can be raised about the conclusions drawn from any experiment. We became more certain that our conclusions are "facts" when we find several different types of experiments support the same conclusions. The "facts" of physiology that you learned this year will change as scientific experimentation continues, but your understanding of the process of science will help you to decide how much reliance to place on the results of future experiments that you hear about.