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Scientific Investigations: The Scientific Method

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1.5: Scientific Investigations
What Turned the Water Orange?
If you were walking in the woods and saw this stream, you probably would wonder what made
the water turn orange. Is the water orange because of something growing in it? Is it polluted with
some kind of chemicals? To answer these questions, you might do a little research. For example,
you might ask local people if they know why the water is orange, or you might try to learn more
about it online. If you still haven't found answers, you could undertake a
scientific investigation
. In short, you could "do"
science
.
Figure 1.5.11.5.1:Rio Tinto river
"Doing"
Science
Science
is more about doing than knowing. Scientists are always trying to learn more and gain a better
understanding of the natural world. There are basic methods of gaining knowledge that is
common to all of
science
. At the
heart
of
science
is the
scientific investigation
.A
scientific investigation
is a plan for asking questions and testing possible answers in order to advance scientific
knowledge.
Figure 1.5.21.5.2 outlines the steps of the
scientific method
.
Science
textbooks often present this simple, linear "recipe" for a
scientific investigation
. This is an oversimplification of how
science
is actually done, but it does highlight the basic plan and purpose of any
scientific investigation
: testing ideas with
evidence
. We will use this flowchart to help explain the overall format for scientific inquiry.
Science
is actually a complex endeavor that cannot be reduced to a single, linear sequence of steps, like
the instructions on a package of cake mix. Real
science
is nonlinear, iterative (repetitive), creative, unpredictable, and exciting. Scientists often
undertake the steps of an investigation in a different sequence, or they repeat the same steps
many times as they gain more information and develop new ideas. Scientific investigations often
raise new questions as old ones are answered. Successive investigations may address the same
questions but at ever-deeper levels. Alternatively, an investigation might lead to an unexpected
observation
that sparks a new question and takes the research in a completely different direction.
Knowing how scientists "do"
science
can help you in your everyday life, even if you aren't a scientist. Some steps of the scientific
process — such as asking questions and evaluating
evidence
— can be applied to answering real-life questions and solving practical problems.
Figure 1.5.21.5.2: The
Scientific Method
: The
scientific method
is a process for gathering data and processing information. It provides well-defined steps to
standardize how scientific knowledge is gathered through a logical, rational problem-solving
method. This diagram shows the steps of the
scientific method
, which are listed below.
Making Observations
A
scientific investigation
typically begins with observations. An
observation
is anything that is detected through human senses or with instruments and measuring devices
that enhance human senses. We usually think of observations as things we see with our eyes, but
we can also make observations with our sense of
touch
, smell, taste, or
hearing
. In addition, we can extend and improve our own senses with instruments such as thermometers
and microscopes. Other instruments can be used to sense things that human senses cannot detect
at all, such as ultraviolet light or radio waves.
Sometimes chance observations lead to important scientific discoveries. One such
observation
was made by the Scottish biologist Alexander Fleming (Figure 1.5.31.5.3) in the 1920s.
Fleming's name may sound familiar to you because he is famous for the discovery in question.
Fleming had been growing a certain type of
bacteria
on glass plates in his lab when he noticed that one of the plates had been contaminated with
mold. On closer examination, Fleming observed that the area around the mold was free of
bacteria
.
Figure 1.5.31.5.3: Alexander Fleming experimenting with penicillin and
bacteria
in his lab in the 1940s.
Asking Questions
Observations often lead to interesting questions. This is especially true if the observer is thinking
like a scientist. Having scientific training and knowledge is also useful. Relevant background
knowledge and logical thinking help make sense of observations so the observer can form
particularly salient questions. Fleming, for example, wondered whether the mold — or some
substance it produced — had killed
bacteria
on the plate. Fortunately for us, Fleming didn't just throw out the mold-contaminated plate.
Instead, he investigated his question and in so doing, discovered the antibiotic penicillin.
Hypothesis
Formation
To find the answer to a question, the next step in a
scientific investigation
typically is to form a
hypothesis
.A
hypothesis
is a possible answer to a scientific question. But it isn’t just any answer. A
hypothesis
must be based on scientific knowledge. In other words, it shouldn't be at odds with what is
already known about the natural world. A
hypothesis
also must be logical, and it is beneficial if the
hypothesis
is relatively simple. In addition, to be useful in
science
,a
hypothesis
must be testable and falsifiable. In other words, it must be possible to subject the
hypothesis
to a test that generates
evidence
for or against it, and it must be possible to make observations that would disprove the
hypothesis
if it really is false.
A
hypothesis
is often expressed in the form of prediction: If the
hypothesis
is true, then B will happen to the
dependent variable
. Fleming's
hypothesis
might have been: "If a certain type of mold is introduced to a particular kind of
bacteria
growing on a plate, the
bacteria
will die." Is this a good and useful
hypothesis
? The
hypothesis
is logical and based directly on observations. The
hypothesis
is also simple, involving just one type each of mold and
bacteria
growing on a glass plate. This makes it easy to test. In addition, the
hypothesis
is falsifiable. If
bacteria
were to grow in the presence of the mold, it would disprove the
hypothesis
if it really is false.
Hypothesis
Testing
Hypothesis
testing is at the
heart
of a
scientific investigation
. How would Fleming test his
hypothesis
? He would gather relevant data as
evidence
.
Evidence
is any type of data that may be used to test a
hypothesis
. Data (singular,
datum)
are essentially just observations. The observations may be measurements in an
experiment
or just something the researcher notices. Testing a
hypothesis
then involves using the data to answer two basic questions:
1. If my
hypothesis
is true, what would I expect to observe?
2. Does what I actually observe match what predicted?
A
hypothesis
is supported if the actual observations (data) match the expected observations. A
hypothesis
is refuted if the actual observations differ from the expected observations.
Testing Fleming's
Hypothesis
To test his
hypothesis
that the mold kills
bacteria
, Fleming grew colonies of
bacteria
on several glass plates and introduced mold to just some of the plates. He subjected all of the
plates to the same conditions except for the introduction of mold. Any differences in the growth
of
bacteria
on the two groups of plates could then be reasonably attributed to the presence/absence of mold.
Fleming's data might have included actual measurements of bacterial colony size, like the data
shown in the data table below, or they might have been just an indication of the presence or
absence of
bacteria
growing near the mold. Data like the former, which can be expressed numerically, are called
quantitative data
. Data like the latter, which can only be expressed in words, such as present or absent, are called
qualitative data
.
Table 1.5.11.5.1: Hypothetical data of bacterial growth on plat
Bacterial Plate Identification Number
Introduction of Mold to Plate?
1
yes
2
yes
3
yes
4
yes
5
yes
Table 1.5.11.5.1: Hypothetical data of bacterial growth on plat
Bacterial Plate Identification Number
Introduction of Mold to Plate?
6
no
7
no
8
no
9
no
10
no
Analyzing and Interpreting Data
The data scientists gather in their investigations are raw data. These are the actual measurements
or other observations that are made in an investigation, like the measurements of bacterial
growth shown in the data table above. Raw data usually must be analyzed and interpreted before
they become
evidence
to test a
hypothesis
. To make sense of raw data and decide whether they support a
hypothesis
, scientists generally use statistics.
There are two basic types of statistics:
descriptive statistics
and
inferential statistics
. Both types are important in scientific investigations.
 Descriptive statistics
describe and summarize the data. They include values such as the mean, or average,
value in the data. Another basic descriptive statistic is the standard deviation, which gives
an idea of the spread of data values around the mean value.
Descriptive statistics
make it easier to use and discuss the data and also to spot trends or patterns in the data.

Inferential statistics
help interpret data to test hypotheses. They determine how likely it is that the actual
results obtained in an investigation occurred just by chance rather than for the reason
posited by the
hypothesis
. For example, if
inferential statistics
show that the results of an investigation would happen by chance only 5 percent of the
time, then the
hypothesis
has a 95 percent chance of being correctly supported by the results. An example of a
statistical
hypothesis
test is a t-test. It can be used to compare the mean value of the actual data with the
expected value predicted by the
hypothesis
. Alternatively, a t-test can be used to compare the mean value of one group of data with
the mean value of another group to determine whether the mean values are significantly
different or just different by chance.
Assume that Fleming obtained the raw data shown in the data table above. We could use a
descriptive statistic such as the mean area of bacterial growth to describe the raw data. Based on
these data, the mean area of bacterial growth for plates with mold is 56 mm2, and the mean area
for plates without mold is 69 mm2. Is this difference in bacterial growth significant? In other
words, does it provide convincing
evidence
that
bacteria
are killed by the mold or something produced by the mold? Or could the difference in mean
values between the two groups of plates be due to chance alone? What is the likelihood that this
outcome could have occurred even if mold or one of its products does not kill
bacteria
? A t-test could be done to answer this question. The p-value for the t-test analysis of the data
above is less than 0.05. This means that one can say with 95% confidence that the means of the
above data are statistically different.
Drawing Conclusions
A statistical analysis of Fleming's
evidence
showed that it did indeed support his
hypothesis
. Does this mean that the
hypothesis
is true? No, not necessarily. That's because a
hypothesis
can never be proven conclusively to be true. Scientists can never examine all of the possible
evidence
, and someday
evidence
might be found that disproves the
hypothesis
. In addition, other hypotheses, as yet unformed, may be supported by the same
evidence
. For example, in Fleming's investigation, something else introduced onto the plates with the
mold might have been responsible for the death of the
bacteria
. Although a
hypothesis
cannot be proven true without a shadow of a doubt, the more
evidence
that supports a
hypothesis
, the more likely the
hypothesis
is to be correct. Similarly, the better the match between actual observations and expected
observations, the more likely a
hypothesis
is to be true.
Many times, competing hypotheses are supported by
evidence
. When that occurs, how do scientists conclude which
hypothesis
is better? There are several criteria that may be used to judge competing hypotheses. For
example, scientists are more likely to accept a
hypothesis
that:
 explains a wider variety of observations.
 explains observations that were previously unexplained.
 generates more expectations and is thus more testable.
 is more consistent with well-established theories.
 is more parsimonious, that is, is a simpler and less convoluted explanation.
Correlation
-Causation Fallacy
Many statistical tests used in scientific research calculate correlations between variables.
Correlation
refers to how closely related two data sets are, which may be a useful starting point for further
investigation. However,
correlation
is also one of the most misused types of
evidence
, primarily because of the logical fallacy that
correlation
implies causation. In reality, just because two variables are correlated does not necessarily mean
that either variable causes the other.
A simple example can be used to demonstrate the
correlation
-causation fallacy. Assume a study found that both ice cream sales and burglaries are correlated;
that is, rates of both events increase together. If
correlation
really did imply causation, then you could conclude that ice cream sales cause burglaries or vice
versa. It is more likely, however, that a third variable, such as the weather, influences rates of
both ice cream sales and burglaries. Both might increase when the weather is sunny.
An actual example of the
correlation
-causation fallacy occurred during the latter half of the 20th century. Numerous studies showed
that women taking hormone replacement therapy (HRT) to treat menopausal symptoms also had
a lower-than-average incidence of coronary
heart
disease
(CHD). This
correlation
was misinterpreted as
evidence
that HRT protects women against CHD. Subsequent studies that controlled other factors related
to CHD disproved this presumed causal connection. The studies found that women taking HRT
were more likely to come from higher socio-economic groups, with better-than-average diets and
exercise regimens. Rather than HRT causing lower CHD incidence, these studies concluded that
HRT and lower CHD were both effects of higher socioeconomic status and related lifestyle
factors.
Communicating Results
The last step in a
scientific investigation
is communicating the results to other scientists. This is a very important step because it allows
other scientists to try to repeat the investigation and see if they can produce the same results. If
other researchers get the same results, it adds support to the
hypothesis
. If they get different results, it may disprove the
hypothesis
. When scientists communicate their results, they should describe their methods and point out
any possible problems with the investigation. This allows other researchers to identify any flaws
in the method or think of ways to avoid possible problems in future studies.
Repeating a
scientific investigation
and reproducing the same results is called
replication
. It is a cornerstone of scientific research.
Replication
is not required for every investigation in
science
, but it is highly recommended for those that produce surprising or particularly consequential
results. In some scientific fields, scientists routinely try to replicate their own investigations to
ensure the reproducibility of the results before they communicate them.
Scientists may communicate their results in a variety of ways. The most rigorous way is to write
up the investigation and results in the form of an article and submit it to a peer-reviewed
scientific journal for publication. The editor of the journal provides copies of the article to
several other scientists who work in the same field. These are the peers in the peer-review
process. The reviewers study the article and tell the editor whether they think it should be
published, based on the validity of the methods and significance of the study. The article may be
rejected outright, or it may be accepted, either as is or with revisions. Only articles that meet high
scientific standards are ultimately published.
Review
1. Outline the steps of a typical
scientific investigation
.
2. What is a scientific
hypothesis
? What characteristics must a
hypothesis
have to be useful in
science
?
3. Explain how you could do a
scientific investigation
to answer this question: Which of the following surfaces in my home has the most
bacteria
: the house phone, TV remote, bathroom sink faucet, or outside door handle? Form a
hypothesis
and state what results would support it and what results would refute it.
4. Use the Table 1.5.11.5.1 above that shows data on the effect of mold on bacterial
growth to answer the following questions
A. Look at the areas of bacterial growth for the plates in just one group – either with
mold (plates 1-5) or without mold (plates 6-10). Is there a variation within the
group? What do you think could be possible sources of variation within the
group?
B. Compare the area of bacterial growth for plate 1 vs. plate 7. Does this appear to be
more of a difference between the mold group vs. the no mold group than if you
compared plate 5 vs. plate 6? Using these differences among the individual data
points, explain why it is important to find the mean of each group when analyzing
the data.
C. Why do you think it would be important for other researchers to try to replicate
the findings in this study?
5. A scientist is performing a study to test the effects of an anticancer
drug in mice with tumors. They look in the cages and observes that the mice that
received the drug for two weeks appear more energetic than those that did not receive the
drug. At the end of the study, the scientist performs surgery on the mice to determine
whether their tumors have shrunk. Answer the following questions about the
experiment
.
A. Is the
energy
level of the mice treated with the drug a qualitative or quantitative
observation
?
B. At the end of the study, the scientist measures the size of the tumors. Is this
qualitative or
quantitative data
?
C. Would the size of each
tumor
be considered raw data or
descriptive statistics
?
D. The scientist determines the average decrease in
tumor
size for the drug-treated group. Is this raw data,
descriptive statistics
, or
inferential statistics
?
E. The average decrease in
tumor
size in the drug-treated group is larger than the average decrease in the untreated
group. Can the scientist assume that the drug shrinks tumors? If not, what do they
need to do next?
6. Do you think results published in a peer-reviewed scientific journal are more or less
likely to be scientifically valid than those in a self-published article or book? Why or why
not
7. Explain why real
science
is usually “nonlinear”?
Explore More
Watch this TED talk for a lively discussion of why the standard
scientific method
is an inadequate
model
of how
science
is really done.
Attributions
1. Rio Tinto River by Carol Stoker, NASA, public
domain
via Wikimedia Commons
2.
Scientific Method
by OpenStax, licensed CC BY 4.0
3. Alexander Flemming by Ministry of Information Photo Division Photographer, public
domain
via Wikimedia Commons
4. Text adapted from
Human Biology
by CK-12 licensed CC BY-NC 3.0
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