1.1: The Scientific Method - Biology

Biologists, and other scientists, study the world using a formal process referred to as the scientific method. The scientific method was first documented by Sir Francis Bacon (1561–1626) of England, and can be applied to almost all fields of study. The scientific method is founded upon observation, which then leads to a question and the development of a hypothesis which answers that question. The scientist can then design an experiment to test the proposed hypothesis, and makes a prediction for the outcome of the experiment, if the proposed hypothesis is true. In the following sections, we will use a simple example of the scientific method, based on a simple observation of the classroom being too warm.

Proposing a Hypothesis

A hypothesis is one possible answer to the question that arises from observations. In our example, the observation is that the classroom is too warm, and the question taht arises from that observation is why the classroom is too warm. One (of many) hypotheses is “The classroom is warm because no one turned on the air conditioning.” Another hypothesis could be “The classroom is warm because the heating is set too high."

Once a hypothesis has been developed, the scientist then makes a prediction, which is similar to a hypothesis, but generally follows the format of “If . then . .” In our example, a prediction arising from the first hypothesis might be, “If the air-conditioning is turned on, then the classroom will no longer be too warm.” The initial steps of the scientific method (observation to prediction) are outlined in Figure 1.1.1.

Figure (PageIndex{1}): An example of the first steps of the scientific method. Figure by L Gerhart-Barley

Testing a Hypothesis

A valid hypothesis must be testable. It should also be falsifiable, meaning that it can be disproven by experimental results. Importantly, science does not claim to “prove” anything because scientific understandings are always subject to modification with further information. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. Each experiment will have one or more variables and one or more controls. A variable is any part of the experiment that can vary or change during the experiment. The control group contains every feature of the experimental group except it is not given the manipulation that tests the hypothesis. Therefore, if the results of the experimental group differ from the control group, the difference must be due to the hypothesized manipulation, rather than some outside factor. Look for the variables and controls in the examples that follow. To test the first hypothesis, the student would find out if the air conditioning is on. If the air conditioning is turned on but does not work, then the hypothesis that the air conditioning was not turned on should be rejected. To test the second hypothesis, the student could check the settings of the classroom heating unit. If the heating unit is set at an appropriate temperature, then this hypothesis should also be rejected. Each hypothesis should be tested by carrying out appropriate experiments. Be aware that rejecting one hypothesis does not determine whether or not the other hypotheses can be accepted; it simply eliminates one hypothesis that is not valid. Using the scientific method, the hypotheses that are inconsistent with experimental data are rejected.

While this “warm classroom” example is based on observational results, other hypotheses and experiments might have clearer controls. For instance, a student might attend class on Monday and realize they had difficulty concentrating on the lecture. One observation to explain this occurrence might be, “When I eat breakfast before class, I am better able to pay attention.” The student could then design an experiment with a control to test this hypothesis.

Exercise (PageIndex{1})

In the example below, the scientific method is used to solve an everyday problem. Order the scientific method steps (numbered items) with the process of solving the everyday problem (lettered items). Based on the results of the experiment, is the hypothesis correct? If it is incorrect, propose some alternative hypotheses.

  1. Observation
  2. Question
  3. Hypothesis (answer)
  4. Prediction
  5. Experiment
  6. Result
  1. The car battery is dead.
  2. If the battery is dead, then the headlights also will not turn on.
  3. My car won't start.
  4. I turn on the headlights.
  5. The headlights work.
  6. Why does the car not start?

C, F, A, B, D, E

The scientific method may seem overly rigid and structured; however, there is flexibility. Often, the process of science is not as linear as the scientific method suggests and experimental results frequently inspire a new approach, highlight patterns or themes in the study system, or generate entirely new and different observations and questions. In our warm classroom example, testing the air conditioning hypothesis could, for example, unearth evidence of faulty wiring in the classroom. This observation could then inspire additional questions related to other classroom electrical concerns such as inconsistent wireless internet access, faulty audio/visual equipment functioning, non-functional power outlets, flickering lighting, etc. Notice, too, that the scientific method can be applied to solving problems that aren’t necessarily scientific in nature.

1.1: The Scientific Method - Biology

Science deals with testable knowledge about physical phenomena in the universe. The goal of science is to understand how the universe works. Biology focuses on understanding living things. To gain knowledge about nature and physical phenomena, scientists use a particular approach called the method of scientific inquiry or the scientific method.

The scientific method is the best approach we have to understanding the natural world and predicting natural phenomena. Evidence for this claim can be found in the successes of science-based technologies. Take medicine, for example. Prior to the 1700s, most medical practices were based on folk traditions or on ideas promoted by religious leaders. Some of these prescientific remedies worked, but the process for discovering new treatments was a slow and haphazard system of trial and error. Ineffective treatments were often accepted simply because there was no clear procedure for evaluating them. Today, with science-based medicine and public health practices, we have gained unprecedented control over threats to our health. According to the Centers for Disease Control, the average life expectancy in the United States has increased by more than 30 years since 1900.

Scientific inquiry has not displaced faith, intuition, and dreams. These traditions and ways of knowing have emotional value and provide moral guidance to many people. But hunches, feelings, deep convictions, old traditions, or dreams cannot be accepted directly as scientifically valid. Instead, science limits itself to ideas that can be tested through verifiable observations. Supernatural claims that events are caused by ghosts, devils, God, or other spiritual entities cannot be tested in this way.


Your friend sees this image of a circle of mushrooms and excitedly tells you it was caused by fairies dancing in a circle on the grass the night before. Can your friend’s explanation be studied using the process of science?

Experimentation and Interpreting Results

A scientific experiment is a carefully organized procedure in which the scientist intervenes in a system to change something, then observes and interprets the result of the change. Scientific inquiry often involves doing experiments, though not always. For example, a scientist studying the mating behaviors of ladybugs might begin with detailed observations of ladybugs mating in their natural habitats. While this research may not be experimental, it is scientific: it involves careful and verifiable observation of the natural world. The same scientist might then treat some of the ladybugs with a hormone hypothesized to trigger mating and observe whether these ladybugs mated sooner or more often than untreated ones. This would qualify as an experiment because the scientist is now making a change in the system and observing the effects.


This video gives another overview of the scientific method:

1.1 Science and the Natural World

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I wish you every success, and my prayer is for you to have a good year with ALL of your subjects. I hope that someday you will be able to look back at some of the things you learned this course and realize how it contributed to a better understanding of the world around you.


  • Realize that we can know God through the study of His creation
  • Identify the goal of science.
  • Describe how scientists study the natural world.
  • Explain how and why scientists do experiments.
  • Describe types of scientific investigations.
  • Explain what a scientific theory is.



  • dependent variable
    • variable in a scientific experiment that is affected by another variable, called the independent variable
    • possible answer to a scientific question must be falsifiable
    • variable in a scientific experiment that is manipulated by the researcher to investigate its affect on another variable, called the dependent variable
    • anything that is detected with the senses
    • statement describing what always happens under certain conditions in nature
    • the process of a scientific investigation
    • broad explanation that is widely accepted as true because it is supported by a great deal of evidence


    Did you ever wonder why some dogs turn in a circle before they go to sleep or how birds learn to sing their special songs? If you ever asked questions such as these about the natural world, then you were thinking like a scientist. The word science comes from a Latin word that means “knowledge.” Science is a distinctive way of gaining knowledge about the natural world that starts with a question and then tries to answer the question with evidence and logic. Science is an exciting exploration of all the whys and hows that any curious person might have about the world. You can be part of that exploration. Besides your curiosity, all you need is a basic understanding of how scientists think and how science is done, starting with the goal of science.

    Now, before we continue, I want to acknowledge that sometimes you might feel like this while reading through this text:

    Scientific Ideas Can Change

    Science is more of a process than a set body of knowledge. In other words, what we think we know can change based on new evidence. Scientists are always testing and revising their ideas, and as new observations are made, existing ideas may be challenged. Ideas may be replaced with new ideas that better fit the facts, but more often existing ideas are simply revised.

    For example, when Albert Einstein developed his theory of relativity, he didn’t throw out Newton’s laws of motion. Instead, he showed that Newton’s laws are a small piece of a bigger picture. In this way, scientists gradually build an increasingly accurate and detailed understanding of the natural world.

    Here are some scientific ideas that have changed as people gained more knowledge over time:

    • Spontaneous Generation – People used to believe that life came from things like earth after being exposed to sunlight, mud, or slime. They thought that things could “magically” come from nothing. Aristotle (a famous Greek philosopher) based his ideas of this from observations of maggots appearing on rotten meat and barnacles forming on a boat’s hull. It wasn’t until the 1600 and 1700’s that this idea began to be disproved.Here’s an interesting thought about this…scientists now say that something cannot come from nothing. It’s impossible! Everything comes from something. A plant comes from a seed. Maggots come from eggs deposited from flies. Mold grows from spores that came from other mold plants and so on. Yet, many scientists still say our universe came into existence via the “Big Bang” from nothing. Does that make sense? If scientists have proved that something cannot come from nothing, then why do they insist on contradicting themselves with the origin of our universe? Stephen Hawking (a very famous physicist and cosmologist) says that due to quantum gravity the universe could have created itself out of nothing. Yet, even he points out in his book “The Grand Design” that the nothingness he is referring to is actually space filled with vacuum energy. Hmmm…where did the vacuum energy come from then? It gets a bit complicated and into the realm of metaphysics (a branch of philosophy dealing with explaining “What is there?” and “What is it like?”) but still, in my opinion, Mr. Hawking is contradicting his own statement that the universe came from “nothing”. Gravity isn’t nothing. It might not be composed of matter, but it’s still a thing that exists. Vacuum energy isn’t nothing. Where did the energy come from? It’s still a something. Anyway, what I’m trying to point out is that even a scientist who says we came from nothing, doesn’t really back that up. Something cannot come from nothing. That’s just the way it is! Of course, as Christians, we understand that everything was created by God. The fact that spontaneous generation was disproved is just a small piece of the creation puzzle that fits!
    • Phlogiston – Scientists used to believe that anything that caught fire had a special element inside it called phlogiston that was released during burning and made the whole process possible. This was eventually proven wrong.
    • Martian Canals – An astronomer in the 1800’s first saw what he believed to be canals on Mars. Other astronomers and scientists claimed to also see the canals and theories were developed as to what their possible origins and use were. It was even proposed that they were created by an intelligent species for irrigation. Eventually this was discredited! Er, it was, right? Otherwise we’d better prepare for a Martian invasion!

    As you can see, just because scientists or people believe something, doesn’t make it true. Scientific ideas can change or even be throw out with new evidence.


    One neat thing about science is that there is so much left to discover and figure out. Discoveries can be made using the scientific method.

    You might be the next person to figure something out that no one else has before! For example, after trekking through the woods and studying how trees branch in specific ways, 13-year-old Aidan Dwyer created a solar cell tree that produces 20-50% more power than a uniform array of photovoltaic panels. You can read an article about him here:

    Scienctific discoveries don’t have to be made by adults in lab coats. They can be made by anyone through observation, questioning and experimenting. That’s why YOU could be the next person to make a break-through! First, you need to know how to go about it though. That takes knowing how the scientific method works.

    The Scientific Method generally follows the steps listed in Figure below.

    Making Observations

    A scientific investigation typically begins with observations. You make observations all the time. Let’s say you take a walk in the woods and observe a moth, like the one in Figure below, resting on a tree trunk. You notice that the moth has spots on its wings that look like eyes. Yeah, look at those eyes. Think about them when you are going to sleep tonight.

    Anyway, you think the eye spots make the moth look like the face of an owl (or maybe one of those Martians that dug the canals on Mars).

    Asking a Question

    Observations often lead to questions. You might ask yourself why the moth has eye spots that make it look like an owl’s face.

    Forming a Hypothesis (which is just a fancy word for “guess”)

    The next step in a scientific investigation is forming a hypothesis (a guess). 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, and it must be logical. A hypothesis also must be falsifiable. That just means it must be possible to make observations that would disprove the hypothesis if it really is false. Assume you know that some birds eat moths and that owls prey on other birds. From this knowledge, you reason that eye spots scare away birds that might eat the moth. This is your hypothesis.

    Testing the Hypothesis

    To test a hypothesis, you first need to make a prediction based on the hypothesis. A prediction is a statement that tells what will happen under certain conditions. It can be expressed in the form: If A occurs, then B will happen (don’t worry, we aren’t getting into math, even if it sounds like it). Based on your hypothesis, you might make this prediction: If a moth has eye spots on its wings, then birds will avoid eating it.

    Next, you need to get evidence to test your prediction. Evidence is any type of facts that may either agree or disagree with a prediction, so it may either support or disprove a hypothesis. Assume that you gather evidence by making more observations of moths with eye spots. Perhaps you observe that birds really do avoid eating the moths. This evidence agrees with your prediction.

    Drawing Conclusions

    Evidence that agrees with your prediction supports your hypothesis. Does such evidence prove that your hypothesis is true? No a hypothesis cannot be proven conclusively to be true, which just means you can never totally put a 100% end to any doubt or questions about it. This is because you can never examine all of the possible evidence, and someday evidence might be found that disproves the hypothesis (like how good telescopes proved that there are no canals on Mars). Still, the more evidence that supports a hypothesis, the more likely the hypothesis is to be true.

    Communicating Results

    The last step in a scientific investigation is communicating what you have learned with others. This is a very important step because it allows others to test your hypothesis. If other researchers get the same results as yours, they add support to the hypothesis. However, if they get different results, they may disprove the hypothesis. When scientists share their results, they should describe their methods and point out any possible problems with the investigation. For example, while you were observing moths, perhaps your presence scared birds away. This introduces an error into your investigation. You got the results you predicted (the birds avoided the moths while you were observing them), but not for the reason you hypothesized.

    Mentioning the whole canals on Mars issue again, it was discovered that there was an optical illusion that came about because, when a poor-quality telescope views many point-like features, such as craters, they appear to join up to form lines. The original belief in Martian canals was based on an error that other observers were able to point out because the observations were shared. Communicating results can also help scientists to avoid some of the same types of errors in future observations and work.


    Figure below shows a laboratory experiment involving plants. An experiment is a special type of scientific investigation that is performed under controlled conditions, usually in a laboratory. Some experiments can be very simple, but even the simplest contributed important evidence that helped scientists better understand the natural world. An example experiment can be seen here:


    An experiment generally tests how one variable is affected by another. The affected variable is called the dependent variable. In the plant experiment shown above, the dependent variable is plant growth. The variable that affects the dependent variable is called the independent variable. In the plant experiment, the independent variable is fertilizer—some plants will get fertilizer, others will not. In any experiment, other factors that might affect the dependent variable must be controlled. In the plant experiment, what factors do you think should be controlled? (Hint: What other factors might affect plant growth?) (Think: water, light, etc.)

    The next two videos should help you understand variables:

    Like a hypothesis, a model must be evaluated. It is assessed by criteria such as how well it represents the real world, what limitations it has, and how useful it is. The usefulness of a model depends on how well its predictions match observations of the real world. Even when a model’s predictions match real-world observations, however, it doesn’t prove that the model is true or that it is the only model that works.


    With repeated testing, some hypotheses may eventually become scientific theories. A scientific theory is a broad explanation for events that is widely accepted as true. To become a theory, a hypothesis must be tested over and over again, and it must be supported by a great deal of evidence. People commonly use the word theory to describe a guess about how or why something happens. For example, you might say, “I think a woodchuck dug this hole in the ground, but it’s just a theory.” Using the word theory in this way is different from the way it is used in science. A scientific theory is more like a fact than a guess because it is so well-supported. There are several well-known theories in biology, like cell theory and germ theory. Another theory is evolution. Remember though, just because something is a theory, doesn’t mean it’s true. Hundreds of years ago Spontaneous Generation was a theory. New evidence and understanding disproved it.


    For hundreds of years, scientists have been using design ideas from structures in nature. Now, biologists and engineers at the University of California, Berkeley are working together to design a broad range of new products, such as life-saving milli-robots modeled on the way cockroaches run and adhesives based on the amazing design of a gecko’s foot. This process starts with making observations of nature, which lead to asking questions and to the additional aspects of the scientific process.

    Let us know what you thought of this chapter or report a broken link (or video). Leave a comment below!


    • The goal of science is to understand the natural world through systematic study. Scientific knowledge is based on evidence and logic.
    • Scientists gain knowledge through scientific investigations. A scientific investigation is a plan for asking questions and testing possible answers.
    • Scientists use experiments to test hypotheses under controlled conditions. Experiments are often done in a lab.
    • Other types of scientific investigations include natural studies and modeling. They can be used when experiments are difficult to do.
    • Scientific theories are broad explanations that are widely accepted as true. This is because they are supported by a great deal of evidence.



    1. What is science? What is the goal of science?

    2. Outline the steps of a scientific investigation.

    3. What is a scientific hypothesis? What characteristics must a hypothesis have to be useful in science?

    4. Give an example of a scientific question that could be investigated with an experiment. Then give an example of scientific question that could not be investigated in this way.

    5. What might be an advantage of collecting evidence in a natural setting rather than in a lab?

    Apply Concepts

    6. Identify the independent and dependent variables in the following experiment:

    A scientist grew bacteria on gel in her lab. She wanted to find out if the bacteria would grow faster on gel A or gel B. She placed a few bacteria on gel A and a few on gel B. After 24 hours, she observed how many bacteria were present on each type of gel.

    Think Critically

    7. Contrast how the term theory is used in science and in everyday language.

    8. Explain how a hypothesis could become a theory.


    The Points to Consider at the end of each lesson in this book will help you relate what you just learned to what is coming next. The questions will help guide you to the next lesson or chapter. Before reading the next lesson of this chapter, consider these points:

    • Remember the opening photo of red blood cells and green viruses? The blood cells are cells of a living thing. Do you think that viruses are living things? Why or why not?
    • Lab experiments are the main method of gathering evidence in some branches of science. Why might lab experiments not be the best way to study living things, such as wild animals?

    Reading Assignment: Read chapter 1 of Evolution Exposed Biology:

    © CK-12 FoundationLicensed under
    CK-12 Foundation is licensed under Creative Commons AttributionNonCommercial 3.0 Unported (CC BY-NC 3.0)”
    • Terms of Use • Attribution
    Changes/edits were made to the original ck12 biology text by Guest Hollow. Changes are not endorsed by ck12 in any way.

    1.1: The Scientific Method - Biology

    Goal 1.1: Understand Systems, Order, and Organization

    8-9.ES.1.1.1 Explain the scientific meaning of system, order, and organization.

    8-9.ES.1.1.2 Apply the concepts of order and organization to a given system.

    Goal 1.2: Understand Concepts and Processes of Evidence, Models, and Explanations

    8-9.ES.1.2.1 Use observations and data as evidence on which to base scientific explanations.

    8-9.ES.1.2.2 Develop models to explain concepts or systems.

    8-9.ES.1.2.3 Develop scientific explanations based on knowledge, logic, and analysis.

    Goal 1.3: Understand Constancy, Change, and Measurement

    8-9.ES.1.3.1 Measure changes that can occur in and among systems.

    8-9.ES.1.3.2 Analyze changes that can occur in and among systems.

    8-9.ES.1.3.3 Measure and calculate using the metric system.

    Goal 1.6: Understand Scientific Inquiry and Develop Critical Thinking Skills

    8-9.ES.1.6.1 Identify questions and concepts that guide scientific investigations.

    8-9.ES.1.6.2 Utilize the components of scientific problem solving to design, conduct,

    and communicate results of investigations.

    8-9.ES.1.3.3 Measure and calculate using the metric system.

    8-9.ES.1.6.7 Explain the differences among observations, hypotheses, and theories.

    1.1: Science and the Scientific Method

    • Contributed by Martin, Neary, Rinaldo, & Woodman
    • Assistant Professor (Physics) at Queen's University

    Science is the process of describing the world around us. It is important to note that describing the world around us is not the same as explaining the world around us. Science aims to answer the question &ldquoHow?&rdquo and not the question &ldquoWhy?&rdquo. As we develop our description of the physical world, you should remember this important distinction and resist the urge to ask &ldquoWhy?&rdquo.

    The Scientific Method is a prescription for coming up with a description of the physical world that anyone can challenge and improve through performing experiments. If we come up with a description that can describe many observations, or the outcome of many different experiments, then we usually call that description a &ldquoScientific Theory&rdquo. We can get some insight into the Scientific Method through a simple example.

    Imagine that we wish to describe how long it takes for a tennis ball to reach the ground after being released from a certain height. One way to proceed is to describe how long it takes for a tennis ball to drop 1 m, and then to describe how long it takes for a tennis ball to drop 2 m, etc. We could generate a giant table showing how long it takes a tennis ball to drop from any given height. Someone would then be able to perform an experiment to measure how long a tennis ball takes to drop from 1 m or 2 m and see if their measurement disagrees with the tabulated values. If we collected the descriptions for all possible heights, then we would effectively have a valid and testable scientific theory that describes how long it takes tennis balls to drop from any height.

    Suppose that a budding scientist, let&rsquos call her Chloë, then came along and noticed that there is a pattern in the theory that can be described much more succinctly and generally than by using a giant table. In particular, suppose that she notices that, mathematically, the time, (t) , that it takes for a tennis ball to drop a height, (h) , is proportional to the square root of the height: [t propto sqrt]

    Use Chloë&rsquos Theory ( (t propto sqrt) ) to determine how much longer it will take for an object to drop by (2) m than it would to drop by (1) m.

    When we have a proportionality law (with a (propto) ) sign, we can always change this to an equal sign by introducing a constant, which we will call (k) :

    Let (t_<1>) be the time to fall a distance (h_<1>=1: ext) , and (t_<2>) be the time to fall a distance (h_<2>=2: ext) . In terms of our unknown constant, (k) , we have:

    By taking the ratio, (frac>>) , our unknown constant (k) will cancel:

    and we find that it will take (sqrt<2>sim 1.41) times longer to drop by (2) m than it will by (1) m.

    Chloë&rsquos &ldquoTheory of Tennis Ball Drop Times&rdquo is appealing because it is succinct, and it also allows us to make verifiable predictions. That is, using this theory, we can predict that it will take a tennis ball (sqrt 2) times longer to drop from (2) m than it will from (1) m, and then perform an experiment to verify that prediction. If the experiment agrees with the prediction, then we conclude that Chloë&rsquos theory adequately describes the result of that particular experiment. If the experiment does not agree with the prediction, then we conclude that the theory is not an adequate description of that experiment, and we try to find a new theory.

    Chloë&rsquos theory is also appealing because it can describe not only tennis balls, but the time it takes for other objects to fall as well. Scientists can then set out to continue testing her theory with a wide range of objects and drop heights to see if it describes those experiments as well. Inevitably, they will discover situations where Chloë&rsquos theory fails to adequately describe the time that it takes for objects to fall (can you think of an example?).

    We would then develop a new &ldquoTheory of Falling Objects&rdquo that would include Chloë&rsquos theory that describes most objects falling, and additionally, a set of descriptions for the fall times for cases that are not described by Chloë&rsquos theory. Ideally, we would seek a new theory that would also describe the new phenomena not described by Chloë&rsquos theory in a succinct manner. There is of course no guarantee, ever, that such a theory would exist it is just an optimistic hope of physicists to find the most general and succinct description of the physical world. This is a general difference between physics and many of the other sciences. In physics, one always tries to arrive at a succinct theory (e.g. an equation) that can describe many phenomena, whereas the other sciences are often very descriptive. For example, there is no succinct formula for how butterflies look rather, there is a giant collection of observations of different butterflies.

    This example highlights that applying the Scientific Method is an iterative process. Loosely, the prescription for applying the Scientific Method is:

    1. Identify and describe a process that is not currently described by a theory.
    2. Look at similar processes to see if they can be described in a similar way.
    3. Improve the description to arrive at a &ldquoTheory&rdquo that can be generalized to make predictions.
    4. Test predictions of the theory on new processes until a prediction fails.
    5. Improve the theory.

    Physics is a branch of science that (underline) the behavior of the universe. When doing physics, we attempt to answer the question of (underline) things work the way they do.

    Testing a Hypothesis

    A valid hypothesis must be testable. It should also be falsifiable, meaning that it can be disproven by experimental results. Importantly, science does not claim to “prove” anything because scientific understandings are always subject to modification with further information. This step—openness to disproving ideas—is what distinguishes sciences from non-sciences. The presence of the supernatural, for instance, is neither testable nor falsifiable. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. Each experiment will have one or more variables and one or more controls. A variable is any part of the experiment that can vary or change during the experiment. The control group contains every feature of the experimental group except it is not given the manipulation that is hypothesized about. Therefore, if the results of the experimental group differ from the control group, the difference must be due to the hypothesized manipulation, rather than some outside factor. Look for the variables and controls in the examples that follow. To test the first hypothesis, the student would find out if the air conditioning is on. If the air conditioning is turned on but does not work, there should be another reason, and this hypothesis should be rejected. To test the second hypothesis, the student could check if the lights in the classroom are functional. If so, there is no power failure and this hypothesis should be rejected. Each hypothesis should be tested by carrying out appropriate experiments. Be aware that rejecting one hypothesis does not determine whether or not the other hypotheses can be accepted it simply eliminates one hypothesis that is not valid (Figure). Using the scientific method, the hypotheses that are inconsistent with experimental data are rejected.

    While this “warm classroom” example is based on observational results, other hypotheses and experiments might have clearer controls. For instance, a student might attend class on Monday and realize she had difficulty concentrating on the lecture. One observation to explain this occurrence might be, “When I eat breakfast before class, I am better able to pay attention.” The student could then design an experiment with a control to test this hypothesis.

    In hypothesis-based science, specific results are predicted from a general premise. This type of reasoning is called deductive reasoning: deduction proceeds from the general to the particular. But the reverse of the process is also possible: sometimes, scientists reach a general conclusion from a number of specific observations. This type of reasoning is called inductive reasoning, and it proceeds from the particular to the general. Inductive and deductive reasoning are often used in tandem to advance scientific knowledge (Figure).

    The scientific method consists of a series of well-defined steps. If a hypothesis is not supported by experimental data, a new hypothesis can be proposed.

    In the example below, the scientific method is used to solve an everyday problem. Order the scientific method steps (numbered items) with the process of solving the everyday problem (lettered items). Based on the results of the experiment, is the hypothesis correct? If it is incorrect, propose some alternative hypotheses.

    1. Observation
    2. Question
    3. Hypothesis (answer)
    4. Prediction
    5. Experiment
    6. Result
    1. There is something wrong with the electrical outlet.
    2. If something is wrong with the outlet, my coffeemaker also won’t work when plugged into it.
    3. My toaster doesn’t toast my bread.
    4. I plug my coffee maker into the outlet.
    5. My coffeemaker works.
    6. Why doesn’t my toaster work?

    Scientists use two types of reasoning, inductive and deductive reasoning, to advance scientific knowledge. As is the case in this example, the conclusion from inductive reasoning can often become the premise for inductive reasoning.

    Decide if each of the following is an example of inductive or deductive reasoning.

    1. All flying birds and insects have wings. Birds and insects flap their wings as they move through the air. Therefore, wings enable flight.
    2. Insects generally survive mild winters better than harsh ones. Therefore, insect pests will become more problematic if global temperatures increase.
    3. Chromosomes, the carriers of DNA, separate into daughter cells during cell division. Therefore, DNA is the genetic material.
    4. Animals as diverse as humans, insects, and wolves all exhibit social behavior. Therefore, social behavior must have an evolutionary advantage.

    The scientific method may seem too rigid and structured. It is important to keep in mind that, although scientists often follow this sequence, there is flexibility. Sometimes an experiment leads to conclusions that favor a change in approach often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds. Scientific reasoning is more complex than the scientific method alone suggests. Notice, too, that the scientific method can be applied to solving problems that aren’t necessarily scientific in nature.

    1.1: The Scientific Method - Biology

    Get to know your table - 5 min

    Pass out lab books
    Explain task - Draw the maggot and make 10 good scientific observations

    Warm Up: Find your new seat and write the names of your group in your lab book.

    In groups whiteboard the top 5 observations.

    Share out with class. Make a list of good characteristics of a scientific observation.

    Warm Up: Explain in your own words what a solar eclipse actually is.

    What do we need to know to solve the “What is the PMI of the body in the wetlands?
    Brainstorm - Whiteboard

    Using the Dichotomous Key learn how to identify a maggot.

    Go outside and collect data and flies.

    Bring back in and place in the freezer.

    Warm up: Write a hypothesis based on your data gathered from the past 4 days. What is the difference between a hypothesis and a theory?

    Measure individual maggots and create the class data sheet.

    Use information on desk - various maggot references in folders.

    Watch Part of - Dirty jobs, Bug Detective.

    Explain how solving a a mystery is can be considered science.

    Discuss what a good discussion should include. How do we communicate this?

    Discuss rubric for writing a conclusion.

    Look at all data. Write a rough draft in class.

    HW: Write conclusion - Due Friday

    Warm Up: Use a computer to log into Schoology. Complete the "uploading a picture:
    Schoology Link

    Chapter 1: The Scientific Method

    Figure 1. Deductive and inductive reasoning in the scientific method. Making sense of the natural world begins with observations. Left) As we collect observations of the world, we can begin to make general predictions (or perceptions) regarding phenomena. This process is known as inductive reasoning, making general predictions from specific phenomena. From these generalized perceptions of reality, specific predictions can be deduced using logic, generating hypotheses. Middle) Experimentation allows researchers to test the predictions of the hypotheses. If a hypothesis is falsified, that is another observation which adds to our general perception of reality. Right) As more and more similar but different experiments reinforce a specific prediction, growing support emerges for the development of a scientific theory, another example of inductive reasoning. In turn, a theory can assist in the development of additional, untested hypotheses using deductive reasoning.

    The scientific method was developed in the 17th century as a method of inquiry to acquire new knowledge or modifying our existing understanding of natural phenomena through process of observation and experimentation. Empiricism is a core principle of scientific method, which maintains that true knowledge is best achieved through sensory experience. This method of inquiry allows measurable results from experimentation to be analyzed with predicted results, generated through observations of the natural world. While the details of the scientific method vary among the disciplines, the basic framework consists of observations resulting in the development of hypotheses, experimentation, measurements to test hypotheses, and an analysis of results to support, reject or help modify the hypotheses.

    Figure 2. The Scientific Method. Observations of the natural world lead to questions. Scientific questions generate hypotheses, many of which may be tested through controlled experimentation. Experimentation and analysis allows hypotheses to be falsified, which provide information (or conclusions). The process of scientific experimentation leads to more observations and questions.

    Deductive and Inductive Reasoning

    Science is an interplay between deductive and inductive reasoning (fig. 1). Experimentation is principally based on deductive reasoning, which starts out with a general understanding of a phenomenon and examines known possibilities (or hypotheses) using rules of logic to eliminate (or deduce) false truths to arrive at a specific conclusion. A biological example would be: "All humans are animals. All animals are eukaryotes. All eukaryotes have cells containing a nucleus. Therefore, all human cells have a nucleus." Inductive reasoning is a principle of logic directly opposite of deductive reasoning. Specific phenomena infer general predictions. In our previous example: "All human cells have a nucleus. All eukaryotes have cells containing a nucleus. All animals are eukaryotes. Therefore, all humans are animals." While science is primarily based on deductive reasoning, inductive reasoning does have its place. Observations of nature are specific in nature. As observations of a specific phenomenon amass, a researcher begins to emerge with a general understanding of that phenomenon (inductive inference), which in turn results in the development of specific hypotheses. Once hypotheses are established, experimentation produces results to reject false hypotheses and support unfalsified hypotheses. As a collection of unfalsified hypotheses get researchers closer and closer to 'the truth', inductive reasoning can be used to develop a scientific theory, which explain and make accurate predictions in a wide range of circumstances.

    Developing a Scientific Question

    Observations of nature allows humans to generate a variety of questions (inductive reasoning), some of which can be answered scientifically. A scientific question is: simple, measurable, testable, answerable, and specific in scope. Perhaps one of the great powers of science is simplicity of the questions asked. Testing simple questions provide incremental knowledge, which build upon one another eventually allowing a grander, more accurate, understanding of natural phenomena. Typically, scientific questions have yes/no answers either there is an effect or there isn't. The best experiments stemming from scientific are typically quite specific in scope: "Does x cause y?" A controlled experiment is the gold standard of the scientific method, in which a single variable is changed and the effect is measured. As the effects of individual variables are understood, scientists seek to understand more complex interactions. However, increasing complexity increases uncertainty, and typically reduces predictive power. As a the interactions of a complex scientific phenomenon becomes better understood from the analysis of many related, but simpler questions, a scientific theory may emerge.

    Hypothesis: more than an educated guess

    A hypothesis is a testable explanation for an observable phenomenon. Hypotheses are generated based on previous observations of nature that can not be explained by existing hypotheses or theories, and must be testable and falsifiable. The purpose of testing hypotheses by experimentation is not to prove a hypothesis, but to disprove inaccurate hypotheses.

    Experimentation and data analysis allows scientists to determine whether the hypothesis is a potentially valid explanation of a natural phenomenon or not. In this way, science is revealing the reality of nature by removing falsified hypotheses. In other words, science reveals 'nature' by removing what is 'not-nature'. Hypothesis testing must be observable and repeatable. In this manner, independent research may revisit and retest the hypotheses. Hypotheses may be supported in one experiment, but rejected in separate experiments. When this happens, further experimental repetitions may need to be conducted to analyze the validity of the hypothesis.

    Figure 3. Null and alternative hypotheses. Every experiment simultaneously tests several hypotheses. In an experiment analyzing the effects between two phenomena, the null hypothesis is there is no difference between those phenomena. An alternative hypothesis states that there is a difference between the phenomena. Different alternative hypotheses will include (1) x is not equal to y, (2) x is greater than y, and (3) x is less than y.

    Every experiment tests multiple hypotheses simultaneously (fig. 3). A null hypothesis (H0) states that there is no relationship between two phenomenon. An example of a null hypothesis is, "H0: There will be no relationship between chemical reaction rates." If the null model is rejected, this infers there is a potential relationship between these phenomenon. In this case, one or more alternative hypotheses (HA) can be proposed to explain the relationship between measured phenomena. Two alternative hypotheses for the proposed null hypothesis are: "HA1: As temperature increases, chemical reaction rates decrease." and "HA2: As temperature increases, chemical reaction rates increase." In this example, the alternative hypotheses infer that temperature causes a change in chemical reaction rates. While this may be true, a scientist must be careful to separate correlation from causation.

    Correlation is not causation, or is it?

    Pure causation indicates that x causes y. In our example, an inference of pure causation would be, "Higher temperature causes an increase in chemical reaction rates." This statement is typically true in chemistry. Heating a solution allows chemical reaction to occur more rapidly. Pure causation is typically inferred in many scientific studies, however a scientist should always consider the many different mechanisms that two variables are correlated.

    Figure 4. Possible relationships between correlated phenomena. Direct causation: x causes y. Reverse causation: y causes x. Common causation: z affects both x and y. Cyclic causation: x affects y, and y affects x. Indirect causation: x affects y, but indirectly through another variable, z. Coincidence: x and y are related, but there are no known causal relationships.

    The opposite of pure causation, known as reverse causation, is where a correlation is present because y causes x. An inference of reverse causation in our example would be, "Higher chemical reaction rates increase temperature." If you have used hand warmers, those little plastic bags you break open and heat is generated, you have experienced the effect of chemical reactions generating heat.

    A third possibility is common causation, where x and y are both affected by a third variable. A classic example is that ice cream sales and drowning deaths are positively correlated. We know ice cream sales don't cause drownings. Rather there is a third variable (temperature) that is positively associated with both variables: ice cream and drownings.

    Cyclic causation occurs when there is a feedback between the variables under consideration. A classic biological example is the numbers of predators and prey are dependent upon each other. If predators increase, prey decrease. If prey increase, predators increase. If prey decrease, prey decrease. Deconstructing these relationships is often very difficult.

    Indirect causation happens, when x is correlated with y, but the effect on y is directly affected by another variable, z, which in turn is affected by x. Indirect causation is very commonly found in community ecology. Suppose a researcher is concerned about a predator on the endangered species list. Considering just the numbers of prey is too simplistic of an approach to adequately model predator population. Revisiting the predator/prey relationship, consider a third variable: plant biomass. Prey abundance is correlated with plant abundance. Therefore, predator abundance (y) can be predicted by calculating plant biomass (x), which directly affects prey abundance (z). In other words, plant biomass directly affects prey abundance, which directly affects predator abundance. Predator abundance is indirectly correlated to plant biomass.

    Figure 5. Correlation between rates of autism and organic food sales. These variables are highly correlated, but clearly organic food sales are not responsible for increasing autism rates.

    Occasionally, two variables are correlated simply by coincidence. In any given random statistical comparison, variables will correlate with each other 5% of the time when the confidence interval is at 95%. This is a primary reason that repeating experiments is so important. While it is not uncommon to find correlations between random variables, additional investigations will be able to examine the validity of actual correlation. If many experiments show a correlation between unsuspecting variables, evidence emerges that a real correlation exists. For example, increasing autism rates in the US are positively correlated with organic food sales. Clearly, there is no causation of organic food on autism. More likely, the detection of autism has refined at the same time as an increasing desire for people to eat organic food. It is purely coincidental.

    Proving causality has proven much more challenging than measuring correlation. Much has been written on the topic of causality from the philosophers of ancient Greece to contemporary physicists studying the butterfly effect, a nonlinear feedback system where the smallest changes in the inputs can result in drastic changes in the output. Many philosophers, statisticians and even scientists suggest that it is impossible to prove causality of any effect. We can only infer correlation. While this is true, a well-designed experiment seeks to minimize this uncertainty.

    A well-designed experiment

    The purpose of the experiment is to provide insight into causation by manipulating an experimental variable (or variables), in order to validate or reject competing hypotheses. While the scope and scale of experiments vary widely within science, well-designed scientific experiments have certain characteristics:

    1. An experiment must be repeatable. An experiment that is not repeatable can not be verified, and therefore is simply an observation. An experimental discovery that is reproducible provides high precision between expected and observed results, which in turn supports consensus on the effect.
    2. An experiment contains many replicates. Making a single observation of a phenomenon is not an experiment, but an observation. Hypothesis testing is conducting using statistics, which requires replicates. A replicate is the repetition of an experimental condition on different subjects (or units, more generally), in order to measure and account the variability within the groups examined. Furthermore, statistics assumes replicates are representative of the population being studied, and preferably randomly selected.
    3. An experiment imposes a treatment and assesses the effect. A treatment (also called the experimental variable) is the variable that scientists alter within an experiment. For example, if a study was comparing the effect of sugar consumption on exam scores, the experimental variable would be the sugar. It is the component of the experiment that will differ between the groups. Replicates are assigned to a control group and one or more experimental groups, and the effect of the treatment is assessed. The control group is a random subset of the subjects (or units) to be examined that either do not receive the treatment, or receive the standard treatment. In our example above, it may not be advisable for a group of students to receive no sugar. In this case, they would receive the minimal dosage necessary. An experimental group (or treatmentgroup) receives the experimental treatment. There typically may be several experimental groups, each receiving varying amounts of the treatment, in order to assess the effect of the treatment at different levels. Control groups provide a baseline to compare the change in the experimental groups based on the effect of the treatment.
    4. Ideally, all variables in an experiment are kept constant except the treatment. While this is not always possible, it is the gold standard of experimental design. If an effect of the treatment is found under such circumstances, much more weight is given toward the direct causation of the treatment.

    Types of experiments

    Ideally, scientists conduct a controlled experiment, in which two (or more) groups (or samples) are established, and receive exactly the same treatment except for the alteration of a single variable, the experimental variable. Typically one of the groups, known as the control group, will not have the experimental variable (or receive a placebo – a substance known to have no effect). A group that receives the experimental variable are known as the experimental group. More than one experimental group is generated when a scientist wishes to determine the range of effects of the experimental variable at different concentrations. By constraining all parameters in the control and experimental group(s), but only changing a single parameter (the experimental variable), any measured variation between the groups can be inferred to be a function of the experimental variable.

    Drug trials are classic examples of a controlled experiment. The group of subjects receiving the drug are within an experimental group. Typically, varying dosages of the drug are administered to different experimental groups. Subjects not receiving the drug represent the control group. Typically, subjects within the control group receive a placebo, a simulated or ineffectual treatment. Drug trials are typically run as blind experiments in which the replicates are not aware if they are receiving the actual treatment (drug) or a placebo. In a double-blind study, the experimenters are kept in the dark as well, in order to minimize researcher bias. Interestingly, many subjects within the control group in a double-blind study of drug trials achieve a measurable response (either positive or negative) from the placebo, known as the placebo effect. To measure the placebo effect, an additional control group (known as a negative control) may be assessed, in which they receive no treatment whatsoever. A positive control is a known effect from the experimental treatment, used to confirm to validity of the measurements acquired. A negative control should provide support to the null hypothesis, indicating no effect between the measured phenomena. Often, negative controls can also be used to establish a baseline result, or to subtract a background value from the test sample results.

    Figure 6. Dependent vs. independent variables. This is a useful tool for determining the dependent and independent variables in an experiment. Once you identify the variables in an experiment, plug them into: ___ depends ___. The first word will be the dependent variable and the last will be the independent variable. Dependent variables are the manipulated variables (or inputs) within a controlled experiment, whereas the independent variable is the expected effect (or output).

    In a controlled experiment, the variable in which the inputs are purposefully manipulated is the independent variable, whereas the variable expected to change (output) based on the presence or abundance of the experimental treatment is the dependent variable. Variables that are kept constant for the control and experimental groups is known as a control variable. For example, in a study that is measuring the effect rabbit abundance in the the presence or absence of wolves, the independent variable would be the presence or absence of wolves. In this example the measured effect (or dependent variable) is the abundance of the rabbit population. Many students struggle to differentiate between independent and dependent variables. The easiest way is to complete the statement (fig. 6), " ___ depends on ___." In our example, "The abundance of rabbits depends on the presence or absence of wolves." If you complete the sentence and it makes sense, the former is dependent variable while the latter is the independent variable. Another way to think about it is what is the variable you are controlling (independent variable) and which one are you expecting a response from the input (dependent variable).

    Controlled experiment are sometimes prohibitive to impossible. Consider a researcher analyzing the effect of rainfall on bird diversity in tropical islands. Rainfall cannot be controlled at such a large scale as is required in a controlled experiment, leading the researcher to employ a natural experiment. Natural experiments are considered quasi-experiments, as the manipulation of variables are outside the researcher's control. In a natural experiment, researchers rely on observations of replicates exposed to a variety experimental and control conditions, and infer an effect. The experimental design of natural experiments seeks to select replicates that closely resemble each other as possible, but vary in preferably one factor. In our example, a researcher would select tropical islands of approximately the same size, structure and composition as is possible, but are known to vary in precipitation amounts. Through careful selection of replicates, effects of the independent variable (i.e. rainfall) on the dependent variable (i.e. bird diversity) can be analyzed. Obviously, it is impossible to select islands that are exactly the same in every way. There will always be variation in island size, distance from other islands, plant diversity, topography, and many other factors. Often known variabilities are also included in a more complex statistical model to determine how all of these factors interact. However as these models increase in complexity, their predictive power diminishes exponentially. Determining causation from correlation from natural experiments is challenging at best.

    Figure 7. Correlation between average global temperature and atmospheric carbon dioxide. Analysis of global warming is a natural experiment. While carbon dioxide and global temperatures are highly correlated, and increasing since the mid-1800s, additional controlled experiments are useful in supporting a case for causation.

    Perhaps, the largest scientific controversy of modern times is climate change. The earth is warming, an indisputable fact. The cause of global warming has been a source of contentious debate among scientists and politicians for decades. The root of this controversy is due to the fact analyzing the causal factors of warming at a global scale is conducted as a natural experiment or mathematical model, as we don't have several replicate earth-like planets to control. With that said, presently no national or international scientific organization disagrees with the hypothesis that global warming is human-caused. Moreover, nearly all scientists agree greenhouse gases are the culprit. Ever since the birth of the industrial revolution, humans have been releasing greenhouse gases into the atmosphere at increasing rates. Average global temperature is highly correlated with atmospheric carbon dioxide (fig. 7). At face value this is simply an observation, that doesn't imply causation. Smaller scale controlled experiments can help assist researchers to determine causation. For example, an experimental design including groups of replicate 'atmospheres' in sealed containers with varying amounts of carbon dioxide could be exposed to the same amount of light energy. Any discrepancy in temperatures between the replicates could be attributed to the abundance of carbon dioxide. In this manner, natural experiments serve as observational studies in which larger scale hypotheses can be generated via inductive reasoning. Smaller scale controlled experiments test these assumptions using deductive reasoning and controlled experimentation and hypothesis testing. If observations of these different approaches are equitable, then there is a stronger support for a causal effect.

    Figure 8. Descriptive statistics describe the central tendency and variability of data. The mean (μ), is the measure of the observed average numerical value of a population of data, whereas the standard deviation (σ) is a measure of the variability of a population of data. If data are normally distributed, the first deviation represents 68% of the observations, while the second and third deviations represent 95% and the 99.7% of the observations.

    Data analysis

    Descriptive statistics define observations

    Descriptive statistics summarize two aspects regarding the distribution of data: central tendency and variability (fig. 8). Central tendency is a measure of the distribution's central (or most common) value. The mean of observed data, known as the sample mean (x̅), represents the mathematical average of the data and is calculated as the sum of observations (Σx) divided by the number of observations, known as the sample size (n): x̅ = Σx / n. Where the mean is the calculated middle of the data, the median represents the observed value of the middle. For example, in the data set [1, 2, 3, 6, 7, 7, 9], the median is 6, the middle number in the ordered observations. If there are an even numbers of observations, the middle two observations are averaged. Occasionally a researcher is interested in most common value, or mode, rather estimating the middle. In the previous data set the mode is 7. Analyzing the distribution of the data is necessary to determine the appropriate measure of central tendency. A mean is typically used when there is a large sample size and few outliers (extreme observations). Hypothesis testing of means typically assumes a normal distribution of data, commonly known as the bell shaped curve, in which most observations are made surrounding the central value and become increasingly less frequent further from the center. When data are not normally distributed, the median is considered a better representation of the typical value. For example, analyses of income typically examine the median, as income is typically skewed due to the presence of extremely high and low income values.

    Figure 9. Visualization of how standard deviation (σ) is calculated. Standard deviation (σ) is a cumulative measure of the deviations of observed values (xi) and the sample mean (x̅). To calculate standard deviation, take the square root (√) of the sum (∑) of squared deviations of the sample mean ( x̅ ) from the observed value (xi), divided by the sample size minus one (n-1).

    Variability is a measured deviation of data from the central tendency, and is also calculated in a variety of ways. The most common measure of variability is standard deviation (σ). A low standard deviation indicates a small variation in the data from the mean, where a high standard deviation indicates data are spread across a large range of values. The standard deviation is commonly used as a measure of statistical confidence. Low standard deviation indicates a high confidence that the measured sample represents the population as a whole. For example, if you measured the height of several random people would that represent the variability in height for the whole human race? Standard deviation is inversely proportional to the sample size. More measurements of height provide a smaller standard deviation. Scientific experiments ideally have many replicates to minimize the effect on standard deviation based on small sample sizes. If the standard deviation is acceptably low, this tells us that the sample mean is very close to the population mean.

    Descriptive statistics are useful in identifying typical observations and detecting extreme observations. For example, the mean (μ) height of adult men is 178cm with a standard deviation (σ) of 8cm. Interpreting the mean alongside the standard deviation suggests most adult men (68%) will be 178±8cm tall, between 170 and 186cm. Two standard deviations (2σ) account for 95% of the variation, also known as the 95% confidence interval. So 95% of adult men are between 178±16cm tall, or the 95% confidence interval for human male adult height is between 162cm and 194cm. Three standard deviations (3σ) account for 99.7% of the variation in the data. Nearly all adult men (99.7% to be precise) are predicted to be between 146cm and 202cm.

    Inferential statistics test hypotheses

    While descriptive statistics describes the nature of sampled observations, inferential statistics infer predictions of the larger population that the sample is based on. Experiments produce data, which in turn are used to conduct hypothesis testing to determine the probability of competing hypotheses. Hypothesis testing is a statistical inference that measures the relationships between the control and the experimental groups. The predictions of each hypothesis are compared to the observed phenomena and typically examined with statistical analysis. If the observed phenomena violate the predictions of the hypothesis, the hypothesis is said to be rejected. If observations do not violate the predictions of the hypothesis, the hypothesis is said to be supported. In this case, some scientists prefer the terminology “fail to be rejected” in lieu of supporting a hypothesis. The reasoning behind this is that even though a hypothesis is supported in one experiment, it can be invalidated in further investigations. The main function of experimentation is the falsification of hypotheses (or disproving hypotheses), not proving hypotheses. In this manner, scientists are not revealing nature, but exposing nature by revealing “not nature.” If a difference between the groups is not found, the null model is supported indicating no relationship between the groups, and therefore no effect of the experimental treatment. If hypothesis testing detects a difference between the data sets, this infers that the experimental treatment has produced some effect on the dependent variable, supporting one of the alternative hypotheses. Additional analysis is conducted to quantify the effect, which can be used to make predictions for future experiments.

    To test the competing hypotheses, a researcher must identify a statistical test appropriate for the experimental design. There are many statistical tests, which differ in their assumptions about the data being compared. Are the data continuous (i.e. time) or categorical (i.e. male v. female)? Are you comparing relationships between dependent (i.e. rabbit abundance) and independent variables (i.e. presence or absence of wolves), or comparing different groups (i.e. olympic medal counts of different countries). Are the data normally distributed? Are there equal variances between groups?

    Figure 10. Decision tree for hypothesis testing using Student's t-test. The p-value of a t-test determines if there is a significant difference the sample means between the control and experimental groups. If p ≥ 0.05, there is no statistical difference, supporting the null hypothesis indicating no effect of the treatment on the dependent variable. If p < 0.05, the null hypothesis is rejected, indicating some effect of the treatment on the dependent variable. If the sample mean (x̅) for the control group is larger than the sample mean for the experimental group, the alternative hypothesis suggesting the treatment decreases the dependent variable is supported. Alternatively, is the mean of the experimental group is higher, it is concluded the treatment increased the dependent variable.

    Once an appropriate statistical test is selected, descriptive statistics help researchers identify a relevant test statistic (e.g. mean, median or some measure of variance between the groups). The observed values are used to calculate the test statistic, which is then compared with the expected values of the test statistic under the null hypothesis, by calculating the p-value. The p-value allows us to determine whether or not the test statistics (e.g. means) of the two samples differ “significantly”. When you take a statistics class, you will learn how this statistic is created. For our purposes, it is sufficient to be able to interpret this statistic without calculating it. The p-value is the probability (ranging from zero to one), that infers whether or not the observed test statistics (e.g. means) of two samples are likely to be different and not merely a product of chance. In most biological studies, if the p-value is less that 0.05 we can state that there is, in fact, a “statistical” difference between the two populations. This is a somewhat artificial cut off, but it is one that is widely accepted in this field of study. The smaller the p-value is the stronger the evidence against the null hypothesis and the higher likelihood that the test statistics actually differ, and increasing support for one of the alternative hypotheses.

    Let's see an example by testing the null hypothesis:

    H0: There is no difference between maze completion times in mice that receive water and mice that receive coffee.

    In this experiment, mice would be selected and placed into the two groups: one group given water and the other given coffee. Mice would be allowed to complete an unknown maze with a food treat at the end, with the observations being measured completion times. To test this hypothesis we would conduct an unpaired t-test, which compares the means of two data sets which are not directly related. Mouse A that receives water has no effect on mouse B that receives coffee. Conducting a unpaired t-test allows the researcher to compare the variation (standard deviation) along with the mean to support or reject the null hypothesis by predicting whether or not the observed means differ from each other significantly. While the process of calculating p-value is beyond the scope of this exercise, we can still interpret it.

    If a different experiment were conducted examining maze memorization rates on the two groups, the researcher would compare the difference between in maze times from the first and second run through of the maze of the same mouse (Δtime) with the observation being the first time subtracted from the second time (Δtime). For this test the researcher would conduct a paired t-test, which compares the means of two observations known to be related. In our example, the first and second maze completion times are expected to be correlated because the same mouse is performing. If a difference was found between Δtime in both groups, then a second statistical test could be used to detect a difference in Δtime between mice that received water and those that received coffee.

    If the results from the experiment support a hypothesis, confidence in the validity of the hypotheses enhances, but does not “prove” the hypothesis is actually true. Future experiments may reveal contrary results. For example, if a hypothesis is rejected during one experiment, it may be supported in a subsequent experiment (or vice versa). If the experiment is repeated a large number of times with same result, the hypothesis may be validated by the larger scientific community. Yet, scientific hypotheses are never said to be “proven,” because new data or alternative hypotheses may emerge to disprove previously supported hypotheses.

    Figure 11. Pasteur's experiment testing spontaneous generation and biogenesis. Pasteur invented the swan-necked flask to create an environment known not to grow microorganisms. After sterilizing a nutrient broth in these flasks, he removed the swan necks of the samples in the control group. Microorganisms grew in the control group, but not the experimental group, supporting biogenesis and rejecting spontaneous generation.

    Pasteur's experiment testing spontaneous generation

    Louis Pasteur is best known for his research with microorganisms and invention of process that bears his name, pasteurization, in which liquids such as milk or beer heated to a temperature between 60˚ and 100˚C killed many of the microorganisms that spoiled these liquids. Once pasteurized and sealed, the liquids would no longer spoil. This discovery drove Pasteur to disagree with a commonly held theory of his day, spontaneous generation

    Spontaneous generation predicts that living organisms emerge from non-living matter. Fleas arise from dust or maggots emerge from flesh, all spontaneously without any living organisms interference. The theory sounds ridiculous to us today, but during Pasteur's time it was widely regarded as fact, with a long history (over two millennia) dating back to Aristotle and beyond. Old ideas are hard to change.

    In his development of the process of pasteurization, Pasteur began to disbelieve spontaneous generation in lieu of an alternative hypothesis, biogenesis, hypothesizing all life comes from pre-existing life.

    To test these competing hypotheses (fig. 11), he developed the swan-necked flask, known to prohibit growth microorganisms in a sterilized broth. He hypothesized that the bend in the neck prevented particles in the air coming in contact with the nutrient broth. Tilting the swan-necked flask such that the broth entered in the tube and was exposed to the air particles resulted in a cloudy broth. He created a nutrient broth and inserted the broth into two swan-necked flasks. He boiled the flasks containing the broth to kill any microorganisms. Removing the swan neck from one of the flasks exposing the broth to air.

    The swan-necked flask remained sterile, while the open flask became cloudy indicating the presence of microorganisms. He concluded that microorganisms were incapable of spontaneously generating in a nutrient-rich broth, as the flask not exposed to the air remained sterile. Rather, broth exposed to the air was populated with unseen (and not well understood) microorganisms that multiplied within the broth, supporting the hypothesis of biogenesis over spontaneous generation.

    History of Scientific Thought

    Before closing this chapter, it may be interesting to go back in history and see how science has evolved over time and identify the key scientific minds in this evolution. Although instances of scientific progress have been documented over many centuries, the terms “science,” “scientists,” and the “scientific method” were coined only in the 19 th century. Prior to this time, science was viewed as a part of philosophy, and coexisted with other branches of philosophy such as logic, metaphysics, ethics, and aesthetics, although the boundaries between some of these branches were blurred.

    In the earliest days of human inquiry, knowledge was usually recognized in terms of theological precepts based on faith. This was challenged by Greek philosophers such as Plato, Aristotle, and Socrates during the 3 rd century BC, who suggested that the fundamental nature of being and the world can be understood more accurately through a process of systematic logical reasoning called rationalism . In particular, Aristotle’s classic work Metaphysics (literally meaning “beyond physical [existence]”) separated theology (the study of Gods) from ontology (the study of being and existence) and universal science (the study of first principles, upon which logic is based). Rationalism (not to be confused with “rationality”) views reason as the source of knowledge or justification, and suggests that the criterion of truth is not sensory but rather intellectual and deductive, often derived from a set of first principles or axioms (such as Aristotle’s “law of non-contradiction”).

    The next major shift in scientific thought occurred during the 16 th century, when British philosopher Francis Bacon (1561-1626) suggested that knowledge can only be derived from observations in the real world. Based on this premise, Bacon emphasized knowledge acquisition as an empirical activity (rather than as a reasoning activity), and developed empiricism as an influential branch of philosophy. Bacon’s works led to the popularization of inductive methods of scientific inquiry, the development of the “scientific method” (originally called the “Baconian method”), consisting of systematic observation, measurement, and experimentation, and may have even sowed the seeds of atheism or the rejection of theological precepts as “unobservable.”

    Empiricism continued to clash with rationalism throughout the Middle Ages, as philosophers sought the most effective way of gaining valid knowledge. French philosopher Rene Descartes sided with the rationalists, while British philosophers John Locke and David Hume sided with the empiricists. Other scientists, such as Galileo Galilei and Sir Issac Newton, attempted to fuse the two ideas into natural philosophy (the philosophy of nature), to focus specifically on understanding nature and the physical universe, which is considered to be the precursor of the natural sciences. Galileo (1564-1642) was perhaps the first to state that the laws of nature are mathematical, and contributed to the field of astronomy through an innovative combination of experimentation and mathematics.

    In the 18 th century, German philosopher Immanuel Kant sought to resolve the dispute between empiricism and rationalism in his book Critique of Pure Reason , by arguing that experience is purely subjective and processing them using pure reason without first delving into the subjective nature of experiences will lead to theoretical illusions. Kant’s ideas led to the development of German idealism , which inspired later development of interpretive techniques such as phenomenology, hermeneutics, and critical social theory.

    At about the same time, French philosopher Auguste Comte (1798–1857), founder of the discipline of sociology, attempted to blend rationalism and empiricism in a new doctrine called positivism . He suggested that theory and observations have circular dependence on each other. While theories may be created via reasoning, they are only authentic if they can be verified through observations. The emphasis on verification started the separation of modern science from philosophy and metaphysics and further development of the “scientific method” as the primary means of validating scientific claims. Comte’s ideas were expanded by Emile Durkheim in his development of sociological positivism (positivism as a foundation for social research) and Ludwig Wittgenstein in logical positivism.

    In the early 20 th century, strong accounts of positivism were rejected by interpretive sociologists (antipositivists) belonging to the German idealism school of thought. Positivism was typically equated with quantitative research methods such as experiments and surveys and without any explicit philosophical commitments, while antipositivism employed qualitative methods such as unstructured interviews and participant observation. Even practitioners of positivism, such as American sociologist Paul Lazarsfield who pioneered large-scale survey research and statistical techniques for analyzing survey data, acknowledged potential problems of observer bias and structural limitations in positivist inquiry. In response, antipositivists emphasized that social actions must be studied though interpretive means based upon an understanding the meaning and purpose that individuals attach to their personal actions, which inspired Georg Simmel’s work on symbolic interactionism, Max Weber’s work on ideal types, and Edmund Husserl’s work on phenomenology.

    In the mid-to-late 20 th century, both positivist and antipositivist schools of thought were subjected to criticisms and modifications. British philosopher Sir Karl Popper suggested that human knowledge is based not on unchallengeable, rock solid foundations, but rather on a set of tentative conjectures that can never be proven conclusively, but only disproven. Empirical evidence is the basis for disproving these conjectures or “theories.” This metatheoretical stance, called postpositivism (or postempiricism), amends positivism by suggesting that it is impossible to verify the truth although it is possible to reject false beliefs, though it retains the positivist notion of an objective truth and its emphasis on the scientific method.

    Watch the video: Bio I Ch. Nature of Science u0026 Scientific Methodology (January 2022).