Scientific Method Disprove Hypothesis Statement

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Solving Problems in Biology

First, consider the question: "What is Science?"

A famous naturalist once said, "Without a hypothesis, a geologist might as well go into a gravel pit and count the stones."

A hypothesis is a "tentative proposition which is subject to verification through subsequent investigation....In many cases hypothese are hunches that the researcher has about the existence of relationships between variables." (Verma and Beard, 1981)

The hypothesis is the cornerstone of science, and hypotheses can be constructed and used in different ways.

In studies of complex, multi-factor systems (e.g., ecology and evolution), a hypothetico-deductive approach is often taken. On other areas, such as cellular and molecular biology, developmental biology, and other areas, hypotheses may be reached inductively, and a set of competing hypotheses potentially able to explain a given observed phenomenon may be tested and systematically eliminated until only the most likely explanations remain. To better understand each method, we should first review the differences between inductive and deductive reasoning.

Inductive vs. Deductive Reasoning

Scientists use both inductive and deductive reasoning to address biological problems.

Inductive Reasoning is sometimes called the "from the bottom up" approach. When we use inductive reasoning, our specific observations and measurements may begin to show us a general pattern. This might allow us to formulate a tentative hypothesis that can be further explored, and we might finally end up making some general conclusions.

In this case, one might construct an argument such as:

  • Items X, Y, and Z all have shown to have characteristic W.
  • Therefore, all items in the same class as X, Y and Z probably also have W.

    For example:

  • This bee stung me. It is a hymenopteran.
  • This wasp stung me. It is a hymenopteran.
  • This fire ant stung me. It is a hymenopteran.
  • I'm starting to see a pattern here. All hymenopterans have stingers.

    One potential pitfall here is the "inductive leap": When you make the jump from many specific observations to a general observation, your generalization might not be correct every time.

  • For example, many hymenopterans (stingless bees and ants, male honeybees, etc.) do not have stingers. (You might not discover this unless you test every single hymenopteran species for stinging capability.)

    Although generalizations are certainly useful, the wise investigator is aware that there may be exceptions to a general rule, and even to the possibility that the "general rule" might eventually be found to be wrong more often than not.

    Deductive Reasoning is sometimes called the "from the top down" approach. In this case, we start with a general idea and work down to the more specific.

    Deductive reasoning is used to test existing theories and hypotheses (general ideas) by collecting experimental observations (specific examples) that put those ideas to the test. One of the most useful ways to use this method is to construct a syllogism, a specific type of argument that has three simple steps:

  • Every X has the characteristic Y.
  • This thing in my hand is X.
  • Therefore, this thing has the characteristic Y.

    For example:

  • All wasps have stingers. (General idea that you inductively reached before.)
  • This thing in my hand is a wasp.
  • Therefore, this thing can probably sting me! (specific conclusion)
  • The experiment necessary to test this hypothesis might be painful.

    The results of your study may suggest further experiments. (What types of hymenopterans don't have stingers? Which is the primitive condition: stinger or no stinger? Why has stinglessness persisted?)

    Hypothesis, Theory, and Law.

    The Hypothesis
    • A hypothesis is a tentative explanation for an observed phenomenon.
    • A prediction is based on past experience about the phenomenon. It's an "educated guess" about what you expect to happen.
    • Multiple hypotheses make good science. (If you have only one possible answer, you may bias your experiment and your analysis.)

      Important aspects of the hypothesis...

      • Hypotheses should be testable via experimental procedures or field studies.
      • A hypothesis can be refuted (proven wrong, or falsified), but it never can be proven to be true. (It is impossible to perform enough experiments to be certain that the answer will always be the same, and that the same explanation will hold true every time.) However, if a hypothesis is tested again and again and is never falsified, it may become elevated to the level of a theory.

    The Theory

    • A theory is an hypothesis that has stood the test of time.
    • It is a well-substantiated explanation of some aspect of the natural world.
    • It is an organized system of accepted knowledge that applies in a variety of circumstances to explain a specific set of phenomena or observations.
    • A theory is constantly subject to testing, modification, and even refutation as new evidence and ideas emerge.
    • Theories also have predictive capabilities that guide further investigation.

      Example: The theory of evolution by means of natural selection.

    The Law

    • A natural law is described by a sequence of events in nature that has been observed to occur--without variation--under the same conditions.
    • Natural law is the basis of the experimental method in science, and is dependent upon cause and effect.
    • A natural law predicts that something will happen under a certain set of circumstances, but it does not explain why it happens.

      Example: The Laws of Thermodynamics

      • Energy cannot be created or destroyed, but only changed in form.
      • All systems tend to move towards a state of greater disorder/chaos.
      It is this adherence to devising general ideas from physical, observable evidence and then subjecting those ideas to critical testing to see if they are valid that sets Science apart from other means of "seeking truth."

    Science as Falsification

    German philosopher Karl Popper wrote in his famous essay, Science as Falsification, that it is vulnerability to falsification--not repeated verification--that is the hallmark of truly powerful hypothesis.

  • Scientific experiments are designed to rule out hypotheses that are clearly wrong.
  • This is done by what amounts to a "process of elmination"
      1. Pose competing hypotheses that all can potentially explain an observation

      2. Before you begin, arm yourself with predictions about what you think will happen if you test the hypothesis.

      3. Design careful, rigorous experiments to put each hypothesis to the test.

      4. Carefully analyze the results.

      5. Decide whether the results support or refute the hypothesis you are testing.

      6. If you have multiple hypotheses, this process continues until one hypothesis is the "last man standing".

      7. The hypotheses that are not falsified by experimental testing are provisionally accepted as potential explanations for the observation.

    This process of exclusion is known as falsification.

    Ridiculously simple example:

    • You are an ancient explorer who has just arrived at the shore of the Pacific Ocean. Being an inquiring person, you hypothesize that there are fish in this body of water.
    • The alternative to this hypothesis might be that there are no fish in the water.
    • To test your hypotheses, you sweep a small dip net into the ocean and pull it out.

    • What if you were to put your net in the water many times, and never catch a fish? Does this mean that the hypothesis that there are no fish in the ocean is TRUE?
    • Not necessarily.
    • While none of your trials has falsified the hypothesis that there are no fish in the ocean, you have not performed the infinite number of trials that might be required to know (with this method, at least) that there are no fish in the ocean.
    • In other words, you have not proven your "no fish" hypothesis to be correct.
      You have only failed to prove that it is incorrect.
    Until you do that one trial that nets a fish, the "no fish" hypothesis must be provisionally accepted, because your observable evidence does not suggest otherwise. But the scientist always must be open to the possibility that an unrefuted hypothesis may, at some point, be proven wrong.

    In the example above, it's easy to see that dipping a net into the ocean isn't a very high-tech way to address this problem. But with more advanced technology such as

    • trawling nets
    • underwater cameras
    • sonar
    • submarine exploration
    • ...etc.

    You might well be able to refute the "no fish" hypothesis. Science marches on as technology improves.

    If you're into metaphors, you might compare hypotheses constructed to Popperian standards to be like a castle or fortress...

    It may look well built from the outside, and seem to be perfectly sound. You may be able to add more blocks and mortar (analogous to finding evidence that appears to support your hypothesis: each dip of the net "confirmed" that there are no fish in the Pacific Ocean, right?)

    but until you test the "castle" by actually attacking it... don't really know how strong it is. If it wasn't well constructed, it may end up looking something like this:

    Statistical Hypotheses

    When an observation involves a relationship between two things, statistical tests are used to determine whether the two phenomena are, indeed, related.

    For example, if you want to know whether a new drug actually helps people quit smoking, the exploratory phase will involve a statistical question:

    "Will smokers taking SmokeAwaytm have a higher rate of giving up smoking than smokers given an indistinguishable placebo?"

    In this method, two muturally exclusive hypotheses are compared

    • The null hypothesis states that there is no relationship between the two phenomena (i.e., SmokeAwaytm does not help smokers quit smoking).
    • The alternative hypothesis states that there is a relationship between the two phenomena (SmokeAwaytm does help smokers quit smoking). The alternative hypothesis may be...
      • one-tailed (specifies a direction for the relationship)
        "Smokers given SmokeAwaytm will have a higher rate of quitting smoking than those given a placebo."
      • two-tailed (it does not specify how the two things vary together)
        "The rate of quitting smoking will be different between patients given SmokeAwaytm and patients given a placebo."

      Well-designed and executed experiments will indicate which of these two competing hypotheses should be rejected, and which should be (provisionally) accepted.

    Strong Inference: Competing Hypotheses

    In 1964, John R. Platt coined the phrase "Strong Inference" to describe a method of inductive inference commonly used in certain scientific fields. The method is straightforward, powerful, and has been employed in some of the earliest and most elegant experiments in biology.

    Platt wrote:

      In its separate elements, strong inference is just the simple and old-fashioned method of inductive inference that goes back to Francis Bacon. The steps are familiar to every college student and are practiced, off and on, by every scientist. The difference comes in their systematic application. Strong inference consists of applying the following steps to every problem in science, formally and explicitly and regularly:

        1. Devising alternative hypotheses that potentially explain an observation

        2. Devising a crucial experiment (or several of them), with alternative possible outcomes, each of which will, as nearly as possible, excludes one or more of the hypotheses;

        3. Carrying out the experiment so as to get a clean result;

        4. Recycling the procedure, making subhypotheses or sequential hypotheses to refine the possibilities that remain, and so on.

        It is like climbing a tree. At the first fork, we choose--or, in this case, "nature" or the experimental outcome chooses--to go to the right branch or the left; at the next fork, to go left or right; and so on. There are similar branch points in a "conditional computer program," where the next move depends on the result of the last calculation. And there is a "conditional inductive tree" or "logical tree" of this kind written out in detail in many first-year chemistry books, in the table of steps for qualitative analysis of an unknown sample, where the student is led through a real problem of consecutive inference: Add reagent A; if you get a red precipitate, it is subgroup alpha and you filter and add reagent B; if not, you add the other reagent. B; and so on.

    (Find the full text of this paper HERE.)

    The Differential Diagnosis scene in every episode of "House" is a bit like a cartoony, quick-n-dirty example of strong inference.

    The Scientific Method: Redux

    And so scientific study proceeds in a way that is very different from progress in other fields:
    • observation - The investigator notes a phenomenon that poses a problem/question.
    • hypothesis formulation - The investigator poses hypotheses that could potentially explain the observation. The more competing hypotheses one can come up with, the better. This helps eliminate the bias you might have if you think you already know the answer!
    • prediction - The investigator makes a statement about what s/he believes will happen when the hypothesis is put to the test.
    • experimental design - The investigator designs an experiment which will yield data to either support or refute one or more of the competing hypotheses.
    • data collection - The experiments are run, and data are collected.
    • data analysis - The data are subjected to rigorous analysis to determine whether any deviation from the prediction is truly meaningful, or merely due to chance.
    • conclusion - The investigator determines whether the outcome of the experiment refutes or supports the hypothesis.

      "The process known as the Scientific Method outlines a series of steps for answering questions, but few scientists adhere rigidly to this prescription. Science is a less structured process than most people realize. Like other intellectual activities, the best science is a process of minds that are creative, intuitive, imaginitive, and social. Perhaps science is distinguished by its conviction that natural phenomena, including the processes of life, have natural causes--and by its obsession with evidence. Scientists are generally skeptics." (from Biology by Neil A. Campbell)

    The scientific method, requirement of physical evidence, falsification and, especially, WILLINGNESS TO MODIFY OR EVEN REJECT LONG-HELD IDEAS THAT TURN OUT TO BE WRONG are hallmarks of science, and are what sets them apart from religious faith. The two philosophies are entirely different, and should not be taught in the same context.

    When you're ready, here's the Truth About the Scientific Method. References
    Platt, J. R., 1964, Strong inference. Science 146: 347-353.

  • Throughout the laboratory portion of most Biology laboratories, you will be conducting experiments. Science proceeds by use of the experimental method. This handout provides a summary of the steps that are used in pursuing scientific research. This general method is used not only in biology but in chemistry, physics, geology and other hard sciences.

    To gather information about the biological world, we use two mechanisms: our sensory perception and our ability to reason. We can identify and count the types of trees in a forest with our eyes, we can identify birds in the rainforest canopy with our ears, and we can identify the presence of a skunk with our nose. Touch and taste help us experience the biological world as well. With the information we gather from our senses, we can make inferences using our reason and logic. For instance, you know that you see palm trees in tropical and subtropical regions and can infer that palm trees will not be found in central Maine because of the harshness of our winter.

    Our reason allows us to make predictions about the natural world. Scientists attempt to predict and perhaps control future events based on present and past knowledge. The ability to make accurate predictions hinges on the seven steps of the Scientific Method.

    Step 1. Make observations. These observations should be objective, not subjective. In other words, the observations should be capable of verification by other scientists. Subjective observations, which are based on personal opinions and beliefs, are not in the realm of science. Here’s an objective statement: It is 58 °F in this room. Here’s a subjective statement: It is cool in this room.

    The first step in the Scientific Method is to make objective observations. These observations are based on specific events that have already happened and can be verified by others as true or false.

    Step 2. Form a hypothesis. Our observations tell us about the past or the present. As scientists, we want to be able to predict future events. We must therefore use our ability to reason.

    Scientists use their knowledge of past events to develop a general principle or explanation to help predict future events. The general principle is called a hypothesis. The type of reasoning involved is called inductive reasoning (deriving a generalization from specific details).

    A hypothesis should have the following characteristics:

    • It should be a tentative idea

    • It should agree with available observations

    • It should be kept as simple as possible.

    • It should be testable and potentially falsifiable. In other words, there should be a

    way to show the hypothesis is false; a way to disprove the hypothesis.

    Some mammals have two hindlimbs would be a useless hypothesis. There is no observation that would not fit this hypothesis!

    All mammals have two hindlimbs is a good hypothesis. We would look throughout the world at mammals. When we find whales, which have no hindlimbs, we would have shown our hypothesis to be false; we have falsified the hypothesis.

    When a hypothesis involves a cause-and-effect relationship, we state our hypothesis to indicate there is no effect. A hypothesis, which asserts no effect, is called a null hypothesis. For instance, the drug Celebra does not help relieve rheumatoid arthritis.

    Step 3. Make a prediction. From step 2, we have made a hypothesis that is tentative and may or may not be true. How can we decide if our hypothesis is true?

    Our hypothesis should be broad; it should apply uniformly through time and through space. Scientists cannot usually check every possible situation where a hypothesis might apply. Let’s consider the hypothesis: All plant cells have a nucleus. We cannot examine every living plant and every plant that has ever lived to see if this hypothesis is false. Instead, we generate a prediction using deductive reasoning (generating a specific expectation from a generalization). From our hypothesis, we can make the following prediction: If I examine cells from a blade of grass, each one will have a nucleus.

    Now, let’s consider the drug hypothesis: The drug Celebra does not help relieve rheumatoid arthritis . To test this hypothesis, we would need to choose a specific set of conditions and then predict what would happen under those conditions if the hypothesis were true. Conditions you might wish to test are doses administered, length of time the medication is taken, the ages of the patients and the number of people to be tested.

    All of these conditions that are subject to change are called variables. To gauge the effect of Celebra, we need to perform a controlled experiment. The experimental group is subjected to the variable we want to test and the control group is not exposed to that variable. In a controlled experiment, the only variable that should be different between the two groups is the variable we want to test.

    Let’s make a prediction based on observations of the effect of Celebra in the laboratory. The prediction is: Patients suffering from rheumatoid arthritis who take Celebra and patients who take a placebo (a starch tablet instead of the drug) do not differ in the severity of rheumatoid arthritis. [Note that we base our prediction on our null hypothesis of no effect of Celebra.]

    Step 4. Perform an experiment. We rely again on our sensory perception to collect information. We design an experiment based on our prediction.

    Our experiment might be as follows: 1000 patients between the ages of 50 and 70 will be randomly assigned to one of two groups of 500. The experimental group will take Celebra four times a day and the control group will take a starch placebo four times a day. The patients will not know whether their tablets are Celebra or the placebo. Patients will take the drugs for two months. At the end of two months, medical exams will be administered to determine if flexibility of the arms and fingers has changed.

    Step 5. Analyze the results of the experiment. Our experiment produced the following results: 350 of the 500 people who took Celebra reported diminished arthritis as the end of the period. 65 of the 500 people who took the placebo reported improvement.

    The data appear to show that there was a significant effect of Celebra. We would need to do a statistical analysis to demonstrate the effect. Such an analysis reveals that there is a statistically significant effect of Celebra.

    Step 6. Draw a conclusion. From our analysis of the experiment, we have two possible outcomes: the results agree with the prediction or they disagree with the prediction. In our case, we can reject our prediction of no effect of Celebra. Because the prediction is wrong, we must also reject the hypothesis it was based on.

    Our task now is to reframe the hypothesis is a form that is consistent with the available information. Our hypothesis now could be: The administration of Celebra reduces rheumatoid arthritis compared to the administration of a placebo.

    With present information, we accept our hypothesis as true. Have we proved it to be true? Absolutely not! There are always other explanations that can explain the results. It is possible that the more of the 500 patients who took Celebra were going to improve anyway. It’s possible that more of the patients who took Celebra also ate bananas every day and that bananas improved the arthritis. You can suggest countless other explanations.

    How can we prove that our new hypothesis is true? We never can. The scientific method does not allow any hypothesis to be proven. Hypotheses can be disproven in which case that hypothesis is rejected as false. All we can say about a hypothesis, which stands up to, a test to falsify it is that we failed to disprove it. There is a world of difference between failing to disprove and proving. Make sure you understand this distinction; it is the foundation of the scientific method.

    So what would we do with our hypothesis above? We currently accept it as true. To be rigorous, we need to subject the hypothesis to more tests that could show it is wrong. For instance, we could repeat the experiment but switch the control and experimental group. If the hypothesis keeps standing up to our efforts to knock it down, we can feel more confident about accepting it as true. However, we will never be able to state that the hypothesis is true. Rather, we accept it as true because the hypothesis stood up to several experiments to show it is false.

    Step 7. Report your results. Scientists publish their findings in scientific journals and books, in talks at national and international meetings and in seminars at colleges and universities. Disseminating results is an essential part of the scientific method. It allows other people to verify your results, develop new tests of your hypothesis or apply the knowledge you have gained to solve other problems.


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