Download Chapter 2: Concept 2.1 - Platteville Public Schools

Objectives
Describe the nature of scientific inquiry.
Compare quantitative and qualitative data.
Summarize the nature of discovery science.
Distinguish between observations and inferences.
Explain the term generalization.
Key Terms
observation
data
inference
generalization
Any science textbook, including this one, is packed with information
based on what scientists have discovered in the past. Indeed, science
has built an impressive body of knowledge that continues to increase
and change with new discoveries. Much of what's known is fascinating,
but the real fun in science begins when you turn from what's known to
what's unknown.
Biology blends two main forms of scientific exploration: discovery
science and hypothesis-based science. Discovery science, as you'll read
later in this section, is mostly about describing nature. Hypothesisbased science, as you'll read in Concept 2.2, is mostly about explaining
nature. Most scientists practice a combination of these two approaches.
Science as Inquiry
Biology is defined as the scientific study of life. But what does
scientific mean? What is science? The word is derived from a Latin
verb meaning "to know." In other words, science is a way of knowing. It
is a way to answer questions about the natural world.
At the heart of science is inquiry—people asking questions about what
they observe in nature and actively seeking answers. For example, have
you ever noticed that most houseplants grow toward a light source,
such as a window? Rotate the plant, and its direction of growth will
shift until the leaves again face the window. Such observations inspire
questions. How does the plant sense the direction of light? What
enables the plant to bend toward light as it grows? In what direction
would a plant grow in the dark?
Your own curiosity is the starting point for exploring life through
inquiry. But inquiry means more than asking questions. Inquiry is a
process of investigation, with thoughtful questions leading to a search
for answers. Asking questions is a natural activity for all curious minds,
but even figuring out what to ask takes practice. You can develop this
and other skills that support scientific inquiry through the activities on
the Biology: Exploring Life Web site and through your laboratory
investigations. By the end of this school year, you'll have plenty of
experience with science as a process of inquiry.
Observations and Data
The questions that drive scientific inquiry are based on observations. In
science, observation is the use of the senses—such as vision or hearing
—to gather and record information about structures or processes.
Recorded observations are called data. Put another way, data are items
of information. For example, the penciled marks along a doorframe that
track a child's height at different ages are biological data (Figure 2-2).
Figure 2-2
For several years, a
mother has recorded the
data of her daughter's
growth by marking her
height on a door-frame.
Organizing data in a table
makes the information
easy to track.
All observations depend on human senses. But, without help the senses
are too limited to penetrate some of the most interesting realms of
nature. Scientific instruments vastly increase the range of possible
observations. In astronomy, telescopes reveal craters on the moon. In
biology, microscopes make it possible to observe life that is invisible to
the unaided eye. Other equipment enables humans to observe DNA and
other molecules.
Observations are often recorded as measurements, also called
quantitative data. Scientists worldwide use an international system of
measurements based on the metric system. (See the Skills Activity
Making Measurements.)
Data also may be qualitative—that is, in the form of descriptions
instead of measurements. For example, Jane Goodall spent decades
recording her observations of chimpanzee behavior in a jungle in
Gambia, an east African nation. In addition to keeping careful notes as
data in her field notebooks, Goodall also documented her observations
with photographs and movies. Data can best support science when they
are clearly organized, consistently recorded, and reliable.
What Is Discovery Science?
Jane Goodall's research on chimpanzees is an example of discovery
science (also called descriptive science). Discovery science describes
natural structures or processes as accurately as possible through careful
observation and data collection. For example, students taking part in a
research project to collect and identify organisms in a river represent
discovery science in action. When a biologist uses a microscope to
describe the arrangement of cells in the interior of a leaf, that's
discovery science, too. In 2000, an international team of scientists
announced that they had completed a detailed chemical "map" of
human DNA. Their achievement is discovery science at the molecular
level.
In contrast to the carefully planned mapping of human DNA, observant
people sometimes discover something important about nature entirely
by accident. One famous example is Alexander Fleming's 1928
discovery that certain fungi produce chemicals that kill bacteria.
Fleming, a Scottish physician, was culturing (growing) bacteria for
research in his laboratory. He found that a mold (a type of fungus) had
contaminated some of his cultures of bacteria. As he was discarding the
"spoiled" cultures, Fleming noticed that no bacteria were growing near
the mold. The fungus turned out to be Penicillium, a common mold. It
produces an antibacterial substance that was later named penicillin.
Fleming's accidental discovery revolutionized medicine. Penicillin
proved to be just one of many lifesaving antibiotics that are made by
fungi and other organisms. These drugs help treat strep throat, bacterial
pneumonia, syphilis, and many other diseases caused by bacteria. The
use of antibiotics has greatly extended the average human lifespan in
many countries.
Inferences in Science
A logical conclusion based on observations is called an inference.
Often, a person makes an inference by relating observations to his or
her prior knowledge. For instance, you infer someone is at the door
when you hear the doorbell ring because you know the same thing has
happened before. Examine the picnic table in Figure 2-6 on page 27 of
your textbook. What can you infer from the place settings and other
objects you observe on the table? Can you infer anything from what is
absent? Can you make reasonable inferences about the weather and
time of day when this photograph was taken?
Inferences are important in science because they help refine general
questions into specific questions that can be explored further. For
example, a scientist might ask: "What substance produced by this
particular mold kills bacteria?" However, keep in mind that scientists
are skeptical of inferences that "stretch" far beyond the data. An
example would be inferring, solely from Fleming's observation, that
some molds could be used to produce antibiotics capable of curing
bacterial diseases in humans. It took much more research before this
conclusion was accepted among scientists. Also, it is important not to
confuse inferences with the observations on which they are based.
Hearing the doorbell ring is an observation. Inferring that someone is at
the door, though reasonable, has less certainty. Maybe an electrical
short circuit is causing the bell to ring.
Generalizations in Discovery Science
Carefully recorded observations provide "raw data" for science.
However, some of the biggest breakthroughs in discovery science come
when scientists put together many specific observations to reach a
general conclusion, or generalization. For instance, in the 1800s,
scientists used microscopes to examine bits of tissue from a diversity of
organisms. In both plants and animals, the scientists observed the tiny
units called cells in all of the specimens they studied. Cells were
actually discovered in the 1600s in oak bark. But it was the
accumulation of observations of cells in all organisms examined that
finally gave biologists confidence that they had uncovered a general
feature of life. This generalization that "all living things are made of
cells" became part of what is known as the cell theory, one of the most
important products of discovery science in the nineteenth century.
It is also sometimes possible to generalize from quantitative data. This
usually requires pooling (combining) measurements from a very large
sample. To look for general patterns in measurements, it often helps to
put the data in a graph. For example, the graph in Figure 2-8 compares
the changes in heights of teenage boys and girls over time. Each point
on the graph is an average measurement for many thousand boys or
girls. The graph makes it easier to spot the general pattern that girls, on
average, stop growing at a younger age than boys. Of course, there are
many individual exceptions. Some females do continue to grow well
past the average age when males stop growing. But the generalization
still holds across the very large sample of teens.
Figure 2-8
A graph is a visual way to uncover general
patterns in data. For example, a
generalization from the data graphed here
is that girls stop growing, on average,
before boys do.
Observations, data, inferences, and generalizations all advance people's
understanding of life. But these processes of discovery science are
often just the beginning of a scientific inquiry. For example, trees do
not grow above a certain altitude in several mountain ranges.
Observations of this "treeline" phenomenon in many mountain ranges
in Western North America may lead you to a generalization: At a
particular latitude, trees don't generally grow above a certain altitude.
And you might go on to infer that environmental conditions at high
altitude are unfavorable to tree growth. But which environmental
factors influence tree growth most? Low temperatures? "Thin"
atmosphere? Strong winds? Lack of soil? Or is it some combination of
such factors that limit tree growth? Discovering something interesting
inspires curious minds to seek an explanation. And that's when
hypothesis-based science comes into play.
Concept Check 2.1
1. How does scientific inquiry differ from simply asking questions?
2. Are the data recorded in the table in Figure 2-2 quantitative or
qualitative? Explain.
3. How is Jane Goodall's work an example of discovery science?
4. Describe an observation you made today and an inference you can
make from that observation.
5. How are the terms generalization and observation related?
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