The Process of Science
By Salvatore J. Tavormina
I.
WHAT IS SCIENCE?
Whether you realize it or not, science plays a major role in our society and in our individual
lives. But what, exactly, is science and how does it differ from other fields of study? Many
students think of science as a large body of facts that are often difficult to understand and tedious
to memorize. The purpose of this essay is to provide you with a better understanding of exactly
what science is and how it works.
For scientists, science is an exciting process of discovery; a method for answering questions and
gaining new knowledge. More specifically, we can think of science as one of the ways humans
attempt to study, understand, and appreciate the universe and their place in it. Of course, many
other fields of study, like history, art, music, philosophy, and religion, also make valuable
contributions to man's understanding and appreciation of himself and the world around him.
Each field of study provides us with a unique view or perspective, and a well educated person is
familiar with a wide variety of different perspectives. Because this is a science class, our focus
will be on the scientific perspective.
II.
SOME ASSUMPTIONS OF SCIENCE
Like all fields of study, science is based on certain assumptions. Assumptions are ideas or
conditions that are accepted without proof, basically because any search for knowledge has to
begin somewhere. Usually we make assumptions that seem to correspond with reality; but often
we accept certain assumptions simply because everyone else does. Occasionally, people make
assumptions that do not seem to correspond with reality because they think it might be
interesting or fun to do so. Nevertheless, it is important to realize that assumptions are accepted
without proof, and if we change our assumptions, we may wind up with very different
conclusions.
How knowledgeable are you about the basic assumptions that underlie the process of science? A
few are listed below:
1.
Science assumes that it can provide reliable knowledge about the physical universe
only; in other words, things that can be detected with our senses (sight, sound, smell,
taste, and touch). This is also referred to as the material universe or the natural
world. Science does not attempt to answer questions or provide knowledge about
spiritual, supernatural, metaphysical, or mystical phenomena; in other words,
things that we cannot detect with our senses. Science does not claim these things do
not exist – simply, that they cannot be studied scientifically. They may be the subject
of other fields of study
2.
Science is based on the assumption of common sense perception - the idea that all
individuals perceive the same natural event in basically the same way.
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3.
Science assumes natural causality – the idea that all natural events have natural
causes that can be understood and explained by humans. In other words, the events
of the physical universe can be explained without invoking the direct intervention of
supernatural causes
4.
Science assumes that the physical universe is rational - that events of the physical
universe are predictable because they follow certain natural laws that can be studied
and understood by humans.
5.
Science assumes that the natural laws that govern the events of the physical universe
are uniform through time and space.
III. THE GOALS OF SCIENCE
One of the basic assumptions of science is that the universe is rational. In other words, the
events of the physical universe follow certain predictable patterns and are governed by certain
natural laws. Although scientists are involved in studying countless different aspects of the
physical universe, the basic goal of all science is to learn about and understand these natural
laws. For example:
-
laws that govern the movement of objects
laws that govern chemical reactions
laws that govern atomic reactions
laws that govern heredity
Today, science is a dominant part of our culture. Why is so much time, energy, and money
devoted to learning about these natural laws? Let's look at 3 reasons:
1.
Curiosity is a fundamental characteristic of human nature. A lot of scientific investigation
is driven by simple curiosity about how and why things work the way they do.
2.
Predictability - If we understand how something works, this can help us understand what
will happen under a given set of conditions in the future. For example, if you sit down in
the cockpit of an airplane, and you understand how the airplane works, then you can
accurately predict what will happen if you press or turn each of the various knobs, switches
and buttons.
3.
Control - Understanding how something works is the first step towards controlling what
happens. Returning to our example, if a pilot understands how an airplane works, then she
can control what it does. Humans love to have control over what happens. There is little
doubt that scientific investigation has given us unprecedented knowledge and control over
the physical universe. This is probably the main reason why science is such an important
part of our culture. Unfortunately, this control has been used to harm as well as benefit
ourselves and the world around us. It is essential that humans learn not only how to gain
reliable knowledge, but also how to use it wisely.
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IV. THE METHODS OF SCIENCE
Now we=re ready to examine the methods scientists use to gain knowledge about the natural
laws governing the physical universe.
As an educated adult, you have probably accumulated a considerable amount of knowledge. But
how did you gain this knowledge? And how reliable is it? THINK about these questions for a
few minutes. If you are having difficulty, think of some specific things you know, like Ahorses
have 4 legs@ or Awhen I push a book off a table it falls to the ground@. Then try to explain how
you gained that knowledge and how certain you are it is correct.
So, what are the different methods that people us to gain knowledge about the universe? In
general, people who study these things recognize 4 basic paths to truth or knowledge: Reason,
Authority (either human or supernatural), Intuition (emotions and feelings), and Sense
Perception.
For example, let=s say we want to answer the question ADo palm trees grow in Yellowstone
National Park?@ What are the different methods we can use to answer this question?
1.
REASON – Reason can be used to give us new knowledge that is derived from previous
knowledge. For example, based on previous knowledge about the climate of Yellowstone
and the inability of palm trees to survive long periods of below-freezing temperatures, we
might use our powers of reason to conclude that palm trees do not grow there.
2.
SENSE PERCEPTION - we could travel to Yellowstone and actually look at the trees
there to determine if any palm trees are present. Of course, this raises the question, would
we have to look at every single tree before we made our conclusion?
3.
AUTHORITY - we could look up the answer in a book, or ask someone who has already
been there.
4.
INTUITION - we could go with our gut feelings or emotions.
So, which of these 4 methods do you think is the best and most reliable way to gain knowledge
about the universe? And can any of these methods give us absolute certain knowledge?
For centuries, philosophers have been debating the relative merits of the various methods that
humans use to gain knowledge. Let=s look at a few more questions that we might want to
answer. For each of the questions listed below, decide which method (reason, sense perception,
authority, or intuition) you think would be the best method for answering the question:
- What color shirt is my father wearing today?
- Why do zebras have black and white stripes?
- If a car is traveling at 50 mph, how far will it travel in 2 hours?
- Does AZT prolong the life of people infected with HIV?
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- Does my girlfriend/boyfriend love me?
- Is there life after death?
- Does God exist?
So what did you decide? Did you choose the same method for answering all 7 questions?
Probably not. So, maybe the method that works best depends on what type of question you are
trying to answer, and what type of knowledge you are seeking.
Scientists are interested in predictive knowledge about the physical universe - knowledge that
can help us predict and control future events. They believe the most reliable way to gain this
type of knowledge is through a combination of sense perception and reason. These 2 methods
are combined in a process that is commonly called AThe Scientific Method@.
It is important to realize that not all questions can be answered using the Scientific Method.
Hopefully, an explanation of the Scientific Method will help you understand not only how
scientists answer questions, but also what types of questions science is able to answer. A crucial
step in any scientific investigation is knowing which questions to ask.
Now, let=s look at how the Scientific Method uses sense perception and reason to provide us with
predictive knowledge about the physical universe. For ease of discussion, we will divide the
Scientific Method into 7 steps, and we will illustrate these steps using 2 examples:
EXAMPLE
#1
#2
TOPIC
Cell Structure
Antiviral Therapy
QUESTION
Do all cells have a nucleus?
Does AZT prolong the life of people infected with HIV?
STEP 1 - MAKE OBSERVATIONS
Scientific investigations generally begin with observations. Observations refer to any
information we collect using our senses - sight, sound, smell, taste, and touch. It is very
important that scientists rely on objective observations (observations that can be verified by
others as true or false) not on subjective observations which involve personal opinions or beliefs.
Some examples of objective observations are listed below:
Example #1 - Cell Structure
a.
you look at some human cheek cells and observe that each one has a nucleus
b.
you look at some onion cells and observe that each one has a nucleus
c.
you look at some skin cells from a frog and observe that each one has a nucleus
Example #2 - Antiviral Therapy
You culture HIV in several test tubes, add a different anti-viral drug to each tube, wait
several days, then measure the concentration of virus in each tube. You observe that the
tube with AZT has the lowest concentration of virus.
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Now, let’s look at some other statements to see if you can identify which are objective
observations:
1.
My left index finger is short.
- Not an objective observation; this is an opinion.
2.
My left index finger is shorter than my left middle finger.
- An objective observation; it can be verified as true or false
3.
In humans, the left index finger is shorter than the left middle finger.
- Not an objective observation; we cannot observe all humans.
4.
All adult insects have 6 legs.
- Not an objective observation; we cannot observe all adult insects.
5.
It is warm in this room.
- Not an objective observation; this is an opinion.
6.
It is 80EF in this room.
- An objective observation; it can be verified as true or false.
7.
My dog likes to chase cats.
- Not an objective observation, we cannot observe thoughts or emotions.
8.
Tomorrow, the sun will rise in the east.
- Not an objective observation; we cannot observe something that hasn’t happened yet.
From these examples, it should be clear that objective observations refer to specific events that
have already happened and which can be verified by others as true or false.
STEP 2 - FORM A HYPOTHESIS
Observations can provide us with valuable information about the physical universe.
Unfortunately, they only give us information about the past or present. There is no way we can
observe things that have not yet happened. But scientists, like most people, also want predictive
knowledge about future events. To gain predictive knowledge about future events, we need to
use our powers of reasoning.
Essentially, scientists use their knowledge about past events to develop some type of general
principle or explanation that can help them predict future events. The general principle is
called a hypothesis and the type of reasoning involved is called inductive reasoning:
Inductive reasoning involves using observations about specific events or facts to develop some
type of general principle. People use inductive reasoning throughout their lives - they use their
past experience to develop general principles about how things work. For example, if we went
to a friend=s house and saw an expensive piece of china sitting on the table, few of us would
think of pushing it off the edge because we have a pretty good idea of what would happen (even
though we=ve never actually pushed that particular piece of china off that particular table.)
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A scientific hypothesis should have the following characteristics:
1.
It is a general principle that is uniform through time and space. Therefore, it applies
to an unlimited number of situations.
2.
It is a tentative idea.
3.
It should be in agreement with all the available observations.
4.
It should be kept as simple as possible. Scientists test the simplest explanations first,
if none of those work then they test more complex explanations.
5.
It must be testable and potentially falsifiable. In other words, in theory at least,
there must be a way to show the hypothesis is false. For example, a scientist would
not make the hypothesis, ASome insects have 6 legs@, because if we find insects with 6
legs they support our hypothesis and if we find insects without legs they also support
our hypothesis. In fact, there is no insect we could find that would not support our
hypothesis. In other words, there is no way to disprove this statement. If we find
some insects with six legs, then our statement is a fact, not a hypothesis.
A valid scientific hypothesis is one which has the above characteristics and which has been
derived from our observations using sound inductive reasoning. In other words, there was no
flaw or mistake in the reasoning that was used to develop the hypothesis. Of course, a single set
of observations can be used to develop many different valid hypotheses; and if our hypothesis is
valid (makes sense and agrees with the available observations), this does not necessarily mean it
is true!
Now let=s test your ability to recognize valid hypotheses. Based only on the 3 observations of
cell structure listed at the bottom of page 4, indicate which of the following are valid hypotheses:
1.
The onion cells I looked at have a nucleus.
- Not a valid hypothesis; this statement is not tentative, it is a fact.
2.
All onion cells have a nucleus.
- Valid hypothesis
3.
All cells have a nucleus.
- Valid hypothesis
4.
Some cells have a nucleus.
- Not a valid hypothesis; this statement is not tentative, it is a fact.
5.
Only onion cells have a nucleus.
- Not a valid hypothesis; this statement is not tentative, it is definitely wrong.
Now that we understand what a hypothesis is, let=s try to develop a hypothesis using the
observations we made on antiviral therapy in step 1.
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In this case scientists take an approach that, at first, might seem a little strange. We have some
evidence to suggest that AZT might be effective against HIV, but we haven=t done any human
trials. In human drug studies, scientists take a conservative approach. Even though they think
the drug might work, they assume it does not work until they have evidence in humans that it
does work (rather than assuming it does work until they have evidence that it does not).
Therefore, a scientist interested in studying the effect of AZT in humans, would make the
following hypothesis:
AAZT does not prolong the life of people infected with HIV@.
STEP 3 - MAKE A PREDICTION
In step 2, we saw that a hypothesis is a tentative idea which may or may not be true. So how do
we decide if our idea really is true?
Well, let=s look at one of our hypotheses from step 2: “All cells have a nucleus@.
How can you prove that this statement is true?
Because this is a general principle about cell structure, it applies to an unlimited number of cells.
Since we cannot examine every cell throughout time and space, there is no way to prove this
hypothesis is correct. However, we can test it by applying it to some specific new situations. As
long as the hypothesis allows us to correctly predict the outcome of these new situations, we
accept it. However, if we find a situation where the hypothesis doesn’t work, we reject the
hypothesis and formulate a new hypothesis that is in agreement with all the available evidence.
Remember, scientists are trying to discover general principles that can be used to accurately
predict the outcome of future events.
A prediction is a specific event that you expect will happen, if your hypothesis is correct. It is
derived from your hypothesis using deductive reasoning.
For example, using the hypothesis: AAll cells have a nucleus@
we could make the prediction: AIf I examine 20 cells from an oak leaf, each one will have a
nucleus.@
Now, let=s examine our hypothesis about antiviral therapy:
AAZT does not prolong the life of people infected with HIV.@
In order to test this hypothesis, we need to choose one specific set of conditions to use:
- How many people will we test?
- Which people will we use and what is their age, sex, health, length of infection, etc?
- Where will we conduct the experiment?
- How much AZT will each person receive?
- What other conditions will we use such as diet, level of exercise, other drugs, etc.?
- How long will we run the test?
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Note that the possibilities are almost endless. All the different conditions that are subject to
change during our test are called variables. In a situation like this, where we are trying to
determine the effect of one particular variable (level of AZT) out of all the different variables
that are involved, we need a controlled experiment.
In a controlled experiment, we use 2 groups of subjects:
The experimental group is exposed to the variable we want to test.
The control group is not exposed to the variable we want to test.
Conditions that we determine at the beginning of our experiment are called independent
variables. An independent variable can either be a controlled, meaning the condition is kept the
same for both groups, or uncontrolled, meaning the condition may vary for the 2 groups.
In a controlled experiment, the variable we want to test should be the only uncontrolled variable.
All other independent variables should be controlled (kept as identical as possible in the 2
groups). Why is this important? If we have more than one uncontrolled variable in our
experiment, and we get different results in the experimental and control groups, we won’t know
which uncontrolled variable caused the difference.
Often, one of the most difficult parts of setting up a controlled experiment is trying to make sure
all conditions for the experimental and control groups are the same, except for the one variable
we want to test. In biological experiments it is usually impossible to ensure that 2 groups of
living organisms are identical with respect to all their characteristics - age, genetic make-up,
previous experiences, etc. In this case, biologists often use a large sample size and
randomization (dividing the subjects into 2 groups at random) to help minimize the effect that
these differences might have on the outcome of an experiment.
A placebo (a non-active substance given to the control group in place of a drug) can help
eliminate the role that psychological factors might have on the outcome of the experiment.
A double-blind study, where neither the subjects nor the people collecting the results know
which individuals are in the experimental group and which are in the control group, can also help
eliminate the effects of psychological factors.
The dependent variable is the factor we observe and compare between the 2 groups at the end
of our experiment.
Now, using our knowledge of controlled experiments, let=s describe the conditions we will use to
the test hypothesis, AAZT does not prolong the life of people infected with HIV.@
We’ll use 1,000 people infected with HIV and divide them at random into 2 groups of 500 each.
- One group receives a 500mg AZT tablet twice per day
- The other group receives a 500mg starch tablet twice per day
- Both groups have the same diet and level of exercise
- All other conditions are kept as identical as possible
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- We will carry out a double-blind study
- At the end of 2 years we will count the number of people who have died in each group
Let=s see if you can identify each of the following components of our experimental set-up:
The experimental group is ______________________________________.
The control group is ___________________________________________.
The uncontrolled variable is _____________________________________.
The placebo is ________________________________________________.
Two controlled variables are _______________ and ___________________.
The dependent variable is ________________________________________.
Now that we have described the conditions we plan to use to test our hypothesis, we need to
make a prediction about what should happen if our hypothesis is correct. A valid prediction is
something that logically MUST happen if our hypothesis is correct. This requires the use of
sound deductive reasoning. Based on the hypothesis, AAZT does not prolong the life of people
infected with HIV@, the following would be a valid prediction for the outcome of our test:
“There will be no significant difference between the number of people who die in the
experimental group and the number who die in the control group.”
STEP 4 - CARRY OUT AN EXPERIMENT
In an experiment, we again rely on sense perception to collect information. More specifically,
we find or create the conditions we described when making our prediction, then observe and
record what actually happens under those conditions.
For Example #1, we would actually observe 20 cells from an oak leaf and determine if each one
had a nucleus.
For Example #2, we would take 1,000 people who are HIV positive, divide them into 2 groups of
500 each, give one group AZT while the other group receives the placebo, keep all other
conditions as identical as possible, and record how many people die in each group over the
course of 2 years.
STEP 5 - ANALYZE THE RESULTS OF THE EXPERIMENT
In this step, the scientist tries to determine if the results of the experiment agree with the
prediction. In other words, did our hypothesis allow us to correctly predict the outcome of the
experiment?
In Example #1, where we predicted that each cell we examine from an oak leaf would have a
nucleus, it is fairly straightforward to determine if the results agree with the prediction. Either
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each cell has a nucleus, or it doesn’t.
In Example #2, where we predicted that there would be no significant difference between the
number of people that die in the experimental group and the number that die in the control group,
the analysis of our results is more complicated. To see why, let=s look at some possible results
for our experiment. For each possible result, indicate whether you think the results agree with
our prediction or not.
Number of People who Died after Two Years (Six Possible Results)
Experimental Group
Control Group
Do the Results Agree
(500 people received AZT)
(500 people received placebo)
with our Prediction?
20
20
20
20
20
0
21
23
25
50
100
100
9 Yes
9 Yes
9 Yes
9 Yes
9 Yes
9 Yes
9 No
9 No
9 No
9 No
9 No
9 No
In each case, was there any uncertainty in your mind about whether the results agreed with the
prediction?
Let=s say we have a group of 1,000 HIV positive people and without any treatment at all, 100 of
them will die within 2 years. However, at the start of the experiment, we do not know which 100
people will die. When we divide these 1,000 people into 2 equal groups (let=s call them A and
B), are we certain to get exactly 50 of the people who are going to die in each group? If we get
49 of the people who are going to die in group A and 51 in group B, what would account for the
difference in the number of people who die in each group during the course of the experiment?
Scientists attribute it to sampling error or chance. Following this line of reasoning further,
could we get 40 of the people who are going to die in group A and 60 in group B simply by
chance? Could we get none of the people who are going to die in group A and 100 in group B
simply by chance? (This is sort of like asking, if we toss a balanced coin 100 times, could we
get 100 heads in a row?) In both cases, the answer is YES. Therefore, if group A receives AZT
and group B does not, and after 2 years no people die in group A and 100 people die in group B,
this difference could have been caused simply by chance when the people were divided up at the
beginning of the experiment!
Basically, our problem is that if fewer people die in the experimental group (which receives
AZT) than in the control group (which receives the placebo), there are 3 possible reasons:
1.
The difference could have been caused by the variable we were trying to test, in this
case by the AZT.
2.
The difference could have been caused by some other variable that was not
adequately controlled. For example, maybe the people in the experimental group had
been infected for a shorter period of time.
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3.
The difference could have been caused by chance.
So, in our experiment, if 20 people die in the experimental group and 50 die if the control group,
how do we determine if the difference was caused by the AZT or if it was simply due to chance?
Unfortunately, there is no way to be sure. As we have already seen, even if no people die in the
experimental group and 100 die in the control group, the difference could have been caused by
chance alone. However, we do know 2 things:
1.
The larger the difference between the 2 groups, the less likely it was caused by
chance alone.
2.
The larger the sample size, the less likely we will get large differences between the 2
groups by chance alone.
Even though there is no way to be sure if the difference between the experimental and control
groups was caused by chance alone, scientists can use statistical tests to determine the
probability that chance alone would produce a difference between the 2 groups that is as large
or larger than the one observed. This probability is called the p value.
The p value can range between 0 (no chance something will happen) and 1 (it definitely will
happen). To see how the p value can help us, let=s look at 3 possible results from our
experiment:
Case 1 - We get a very small difference between the experimental and control groups.
Let=s say 20 people die in the experimental group and 21 die in the control group. If you did a
statistical test using these results, would you expect to get a large p value or a small p value?
A small difference between the experimental and control groups will give us a large p value.
This means there is a fairly large probability of getting a difference of this size or larger simply
by chance. Therefore, we conclude that the difference probably was caused by chance alone.
Scientists have arbitrarily chosen p=.05 as a cut-off point. If p is greater than .05, we say the
difference between the experimental group and the control group is not significant (more
precisely, not significant at the .05 level) because there is a high probability it was caused by
chance alone.
It is important to note that a large p valve (p>.05) does not prove the difference was caused by
chance alone, it means there is a high probability that it could have been caused by chance alone.
Case 2 - We get a very large difference between the experimental and control groups.
Let=s say no people die in the experimental group and 100 die in the control group. If you did a
statistical test using these results, would you expect to get a large p value or a small p value?
A large difference between the experimental and control groups will give a small p value. This
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means there is a fairly small probability of getting a difference of this size or larger simply by
chance. Therefore, we conclude the difference was probably not caused by chance alone.
Scientists have arbitrarily chosen p=.01 as a cut-off point. If p is less than .01, we say the
difference between the experimental group and the control group is highly significant (more
precisely, significant at the .01 level) because there is a very small probability it was caused by
chance alone.
It is important to realize that a very small p value (p<.01) does not prove the difference wasn=t
caused by chance alone, it means there is a very small probability it was caused by chance alone.
Case 3 - We get a large difference between the experimental and control groups, but not
a very large difference.
So far we have seen that:
If p is greater than .05, we say the difference is not significant (at the .05 level)
If p is less than .01, we say the difference is highly significant (at the .01 level)
What about when p is between .05 and .01? Scientists consider this an Ainconclusive@ result.
They say the difference is significant, but not highly significant. In most cases, they would want
to collect more data before making a decision.
STEP 6 - MAKE A CONCLUSION
As we have seen, when we analyze the results of an experiment, there are 2 possibilities: either
the results do not agree with our prediction or they do. Let=s use our example dealing with cell
structure to examine these 2 possibilities:
Hypothesis - All cells have a nucleus.
Prediction - If I examine 20 cells from an oak leaf, each one will have a nucleus.
1st possibility - The results of our experiment do not agree with our prediction.
Let=s say the results of our experiment do not agree with our prediction – when we looked at 20
cells from the oak leaf, we found some did not have a nucleus. What should we conclude about
our hypothesis? In this case we reject the hypothesis. This means we have evidence to show the
hypothesis is wrong. We need to revise our hypothesis so that it is in agreement with all the
available evidence. We reject our hypothesis because it was not able to accurately predict the
outcome of our experiment. Scientists are looking for general principles that allow them to
accurately predict the outcome of future events.
2nd Possibility - The results of our experiment agree with our prediction.
What if the results of our experiment agree with our prediction - all of the cells we looked at
from the oak leaf had a nucleus. What should we conclude about our hypothesis? In this case,
we accept our hypothesis. When a scientist accepts a hypothesis, this does not mean he has
decided that it is correct or that it is probably correct. Accepting a hypothesis means that all of
the available evidence supports it. In other words, so far it works. However, this does not
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guarantee that it will continue to work in the future. Once we accept a hypothesis, we can
continue to test it by seeing if it continues to accurately predict the outcome of new situations.
If more and more evidence continues to support our hypothesis, scientists eventually call it a
theory, and ultimately a scientific law. These 3 designations (hypothesis, theory, and law)
indicate general principles that are supported by increasing amounts of evidence. As more and
more evidence supports our general principle or explanation, we become more and more certain
that it provides a reliable basis for predicting future events. However, even scientific laws do not
represent absolute certainty or truth. There are many cases in the history of science where a
scientific law had to be changed or modified when new evidence became available that did not
support it. Scientists must always remain open-minded and ready to change their ideas as new
evidence is collected.
Therefore, when you read a scientific article or a science textbook, do not accept the ideas
presented as certainty or truth. Science tries to provide us with the most likely explanation for
what we observe based on the available evidence. Many of these ideas will be discarded or
modified as scientists collect new data in their search for knowledge about how the physical
universe works.
STEP 7 - REPORT YOUR RESULTS
The final step in the Scientific Method is to report and explain your results. Scientists use a
variety of ways to report their results - discussions with colleagues, attending conferences and
meetings, computers and other electronic media, printed journals and books, etc. A published
article in a scientific journal is the most common way to provide a permanent record of the
results. Reporting and explaining results is essential for the progress of scientific knowledge. It
allows other people to verify your results, develop new tests of your hypothesis, possibly
develop new hypothesis, or apply the knowledge to solving practical problems.
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