Encyclopedia of Evolution.pdf - Online Reading Center

The testing of hypotheses accumulates gradually, but changes
in theory can be rapid.
Scientists accept the simplest hypothesis that will explain
the observations. When given a choice between a simple,
straightforward explanation, and a complex one (especially
one that requires numerous assumptions), scientists will
choose the former. This is referred to as Occam’s Razor,
named after William of Occam (or Ockham), a medieval
English philosopher and theologian.
Scientific research uses null hypotheses and statistical
analysis to determine whether the results might have occurred
by chance and will accept the results only if they are very
unlikely to have occurred by chance. In everyday life, observers
frequently notice patterns in events and objects. Millions
of years of evolution have given the human brains the habit
of looking for patterns. However, these patterns may be the
product of imagination rather than a component of reality.
Scientists are no different from any other people in having
brains that can deceive them into believing false patterns, but
they take special precautions to prevent this from happening—a
set of precautions usually absent from nonscientific
ways of knowing. For example, three good days on the stock
exchange may look like a trend, and some investors would
take it for one. A statistical analysis may show that such a
three-day streak could readily occur by chance. The scientific
method is, therefore, like a self-imposed yoke: It restricts scientists
from wandering off in erroneous directions as cows
or humans are wont to do; and in the process of restricting
them, the scientific method allows scientists to do the useful
work of pulling the cart of knowledge forward.
In order to test a hypothesis about a process, a scientist
must specify what would happen if that process were not
occurring. This null hypothesis is therefore the alternative
to the hypothesis the scientist is investigating. When experiments
are involved, the null hypothesis is usually investigated
by a control, which is just like the experimental treatment in
every way except for the factor being investigated. One of the
earliest, and most famous, examples of a null hypothesis control
was from Italian scholar Francesco Redi’s 16th-century
experiment that tested the hypothesis of biogenesis. Biogenesis
asserts that life comes from preexisting life. If maggots
appear in rotting meat, it must be because flies laid eggs
there. The null hypothesis was that life need not come from
preexisting life—that is, maggots can arise spontaneously
from rotting meat, even in the absence of flies. Redi took two
pieces of meat, put them in two jars, but covered one of the
jars with a screen that excluded flies. Both pieces of meat rotted,
but only the meat in the open jar produced maggots.
Almost anything can happen by chance, once in a while.
In the case of the flies and the maggots, the results are pretty
clear. But in many or most other scientific investigations, the
results are far less clear. How can a scientist be reasonably
sure that the results did not “just happen” by chance? The
science of statistics allows the calculations of probability to
be applied to hypothesis testing.
There are two kinds of error that a scientist can make
regarding these probabilities. The first kind of error (called
Type I error) occurs when the scientist concludes that the
scientific method
results were due to chance, when in reality the hypothesis
was correct. The scientist failed to find something that was
real. This is not considered a serious error, because later
investigation may allow more chances to discover the truth.
The second kind of error (of course, Type II error) occurs
when the scientist concludes that the hypothesis was correct
(“Eureka!”) when in reality the results were due to chance.
This is a more serious kind of error, because the scientist
and his or her peers around the world might conduct further
investigations and waste time and effort under the misguided
notion that the hypothesis was correct. Therefore
scientists try their best to avoid Type II error. Since they can
never be totally sure, scientists universally accept a probability
of 5 percent as the generally acceptable risk for Type
II error. If the probability is less than 1 in 20 (p < 0.05)
that the results could have occurred by chance, then scientists
generally believe the results. The calculations of probability
are quite complex, and most scientists let computers
do these calculations.
Scientists take special precautions to avoid biased observations.
Biased observations occur when the scientist expects
certain results, even wants them to occur, and then tends
to favor them when he or she sees them. To avoid this very
human tendency to see what they want to see, scientists use
objective measurements—temperature, weight, voltage,
for example—rather than subjective assessments and often
design their studies to be blind. That is, the scientist may not
even know the sources of some of the specimens that he or
she measures. One scientist may gather specimens and label
them with simply a number. Another scientist receives the
specimens, identified only by their label numbers, and makes
measurements on them. This is called a blind experiment
because the scientist who is making the measurements cannot
be biased by the knowledge of where the specimens came
from. This is particularly important in drug tests, because
patients may report feeling better, and may actually improve,
if they think they have received the drug (this is called the
placebo effect). Even if the scientist does not tell the patient
which pills are real and which are sugar pills, the scientist can
subconsciously communicate the information, for example by
the tone of voice. In such tests, therefore, a double blind procedure
is routinely used, in which neither the investigator nor
the subjects know which pill is which.
Scientific research frequently involves experimentation.
When possible, scientists conduct experiments. In an experiment,
the scientist imposes conditions upon the phenomena
being studied, so that, to the greatest extent possible, only one
factor is allowed to vary. In a laboratory, all conditions such
as lighting, temperature, and humidity can be controlled. In
the field, conditions may be quite variable, but if the experimental
treatment and the control are side by side, the variability
of all factors except the one being studied might be the
same and therefore cancel out of the analysis.
Experiments are not always possible. Sometimes the
arena of investigation is simply too big. How can one conduct
an experiment with a whole mountain? Actually, some
ecologists in the 1970s studied the effects of clear-cutting,
strip cutting, and burning on the flow of nutrients in stream