Biological field and laboratory methods for measuring the quality of ...

BIOLOGICAL METHODS
sample or a plankton haul. However, m this
section the term "sample" will be used to
denote "a set of observations" - the written
records themselves.
1.1.6 Parameter and statistic
When we attempt to characterize a population,
we realize that we can never obtain a perfect
answer, so we settle for whatever accuracy
and precision that is required. We try to take an
adequately-sized sample and compute a number
from our sample that is representative of the
population. For example, if we are interested in
the population mean, we take a sample and compute
the sample mean. The sample mean is
referred to as a statistic, whereas the population
mean is referred to as a parameter. In general,
the statistic is related to the parameter in much
the same way as the sample is related to the population.
Hence, we speak of population parameters
and sample statistics.
Obviously many samples may be selected
from most populations. If there is variability in
the population, a statistic computed from one
sample will differ somewhat from the same
statistic computed from another sample. Hence,
whereas a parameter such as the population
mean is fixed, the statistic or sample mean is a
variable, and there is uncertainty associated with
it as an estimator of the population parameter
which derives from the variation among samples.
2.0 STUDY DESIGN
2.1 Randomization
In biological studies, the experimental units
(sampling units or sampling points) must be
selected with known probability. Usually,
random selection is the only feasible means of
satisfying the "known probability" criterion.
The question of why known probability is required
is a valid one. The answer is that only by
knowing the probability of selection of a sample
can we extrapolate from the sample to the
population in an objective way. The probability
allows us to place a weight upon an observation
in making our extrapolation to the population.
There is no other quantifiable measure of "how
well" the selected sample represents the
population.
2
Thus our efforts to select a "good" sample
should include an appropriate effort to define
the problem in such a way as to allow us to
estimate the parameter of interest using a sample
of known probability; i.e., a random sample.
The preceding discussion should leave little
doubt that there is a fundamental distinction
between a "haphazardly-selected" sample and a
"randomly-selected" sample. The distinction is
that a haphazardly-selected sample is one where
there is no conscious bias, whereas a randomlyselected
sample is one where there is consciously
no bias. There is consciously no bias because tne
randomization is planned, and therefore bias is
planned out of the study. This is usually accomplished
with the aid of a table of random
numbers. A sample selected according to a plan
that includes random selection of experimental
units is the only sample validly called a random
sample.
Reference to the definition of the term,
sample, at the beginning of the chapter will
remind us that a sample consists of a set of
observations, each made upon an experimental
or sampling unit. To sample randomly, the
entire set of sampling units (population) must be
identifiable and enumerated. Sometimes the task
of enumeration may be considerable, but often
it may be minimized by such conveniences as
maps, that allow easier access to adequate
representation of the entity to be sampled.
The comment has frequently been made that
random sampling causes effort to be put into
drawing samples of little meaning or utility to
the study. This need not be the case. Sampling
units should be defined by the investigator so as
to eliminate those units which are potentially of
no interest. Stratification can be used to place
less emphasis on those units which are of less
interest.
Much of the work done in biological field
studies is aimed at explaining spatial distributions
of population densities or of some
parameter related to population densities and
the measurement of rates of change which
permit prediction of some future course of a
biologically-related parameter. In these cases the
sampling unit is a unit of space (volume, area).
Even in cases where the sampling unit is not a
unit of space, the problem may often be stated