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Solving Problems in Biology

Consider "What is Science?"
How do we know what we know?

Inductive vs. Deductive Reasoning

Scientists use both

    Inductive Reasoning

    Induction involves using many individual observations to make a generalization.
    This approach can be considered to be moving "from the bottom up".

    As in:

    • Item X has characteristic W.
    • Item Y has characteristic W.
    • Item Z has characteristic W.
    • Therefore, all items in the same class as X, Y, and Z should also have characteristic W.

    The Risk of the Inductive Leap

    One could make many individual observations:
    • 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.
    And come to a general conclusion:
    • Therefore, all hymenopterans have stingers.

    But you might already see a potential pitfall here.

    A general conclusion made from many specific observations
    may not always be true.

    Unless you test every every single hymenopteran species for stinging capability,
    you might not discover an exception to your general rule.

    In reality, many hymenopterans (stingless bees and ants, male honeybees, etc.) lack stingers.

Generalizations are certainly useful.
But a wise person knows there may be exceptions to a general rule.
It is even possible that the "general rule" might turn out to be wrong more often than not.
One can avoid this problem with deductive reasoning.

    Deductive Reasoning

    Deduction starts with a general idea and determines whether it applies to a specific observation.
    This approach can be considered to be moving "from the top down".

    As in:

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

    Deduction

    Existing theories and hypotheses (generalizations) can be tested via deduction.
    Experimental observations (specific examples) are collected to test those generalizations.

    A syllogism is a deductive argument with three simple steps:

    • All items of type X have the characteristic Y. (inductive generalization)
    • This thing in my hand is of type X.
    • Therefore, this thing in my hand has the characteristic Y. (specific conclusion)

    For example:

    • All wasps have stingers. (inductively devised generalization)
    • This thing on my hand is a wasp.
    • Therefore, this thing on my hand can probably sting me. (specific conclusion)

    To test this argument, you must now conduct a (potentially painful) experiment.

    The results of your study may suggest further questions.

    • What types of hymenopterans lack stingers?
    • Which is the primitive condition: stinger or no stinger?
    • Why has stinglessness persisted? Is it adaptive?

    The fun has just begun.

Hypothesis, Theory, and Law.

Science is different from other methods of seeking truth in that

You may have heard an acquaintance say he has a "theory" about something.
This usually means they have a hunch based on little more than idle speculation or a "gut feeling".

In science, however, the terms hypothesis, theory, and law have very specific definitions.


Hypothesis


Theory


Natural Law


Science as Falsification

In his classic essay Science as Falsification, German philosopher Karl Popper explained why
vulnerability to falsification--not repeated verification--is the hallmark of truly powerful hypothesis.

A falsifiable (Popperian) hypothesis can be put to a test that could possibly refute it.

In Popperian science, experiments are designed to rule out hypotheses that are clearly wrong.
In essence, this scientific method entails a process of elmination.

This process of exclusion is known as falsification.

In Popperian science

A hypothesis not falsified by experimental results can be provisionally accepted as a potential explanation of the observation.
However, failure to falsify a hypothesis does NOT prove that the hypothesis is true.

Falsification: An Illustration

    An ancient North American explorer arrives at the Pacific Ocean for the first time.
    He knows that fish live in other bodies of water such as lakes and streams.
    • Hypothesis: There are fish in this new body of water.
    • Alternative (competing) hypothesis: There are no fish in this new body of water.

    To test the hypotheses, he sweeps a dip net into the ocean and pulls it out.

    • He repeats this procedure hundreds of times, and never catches a fish.
    • Does this mean "There are no fish in this new body of water" is TRUE?

    The essence of Popperian Science is falsifiability.

  • None of the explorer's hundreds of trials has falsified the "no fish" hypothesis.
  • However, the next trial might yield a fish.
  • The "no fish" hypothesis can be provisionally accepted because observable evidence does not indicate otherwise.
  • But the "no fish" hypothesis cannot be said to be TRUE.
  • The falsification might still be out there.
  • All it takes is the capture of ONE FISH for the "no fish" hypothesis to be rejected.
Stated simply:
  • The explorer has NOT proven his "no fish" hypothesis to be correct.
  • Rather, the explorer has failed to prove his "no fish" hypothesis to be incorrect.
  • This is a subtle, but critical distinction.

The explorer must remain open to the possibility that his unrefuted hypothesis might, at some future time, be refuted.

Future Experimentation

Of course, 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.

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

    Popperian Hypothesis: An Analogy

    One might compare a Popperian hypothesis to castle or fortress.
    It appears well built and perfectly sound.

    Which is the better way to test the strength of the castle?

    • List the ways the castle is fortified to be strong.
    • Attack the castle and try to break it down.

  • Listing castle fortifications is analogous to listing bits of evidence that support a particular hypothesis.

  • Attacking the castle is analogous to performing an experiment that could refute that hypothesis.

    No matter how well built the castle might be, until you actually try to break it down, you don't know if it will stay up.

    If the castle was not "true", it may end up looking something like this.

Strong Inference: Competing Hypotheses

In 1964, John R. Platt coined the phrase strong inference to describe a straightforward, powerful method of addressing a biological problem:
posing multiple, competing hypotheses, any of which could potentially explain an observation.

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, exclude 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 Platt's paper HERE.)

    Strong Inference: Let House Demonstrate

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

    Watch House: One Day, One Room up to 5:00.

    As you watch the excitement, try to determine:

    • What is the observation in this episode?
    • What is the question?
    • What are the competing hypotheses?
    • What are the proposed experiments?
    • What was the result?

    Posing only one hypothesis to explain an observation can lead to
    confirmation bias, the tendency to interpret evidence
    as confirmation of one's own pre-existing ideas about that observation.

    Posing competing hypotheses to explain an observation can help prevent confirmation bias.

    If Hollywood TV writers can do it, so can you.

Hypotheses

About thirty years ago there was much talk that geologists ought only to observe and not theorise;
and I well remember some one saying that at this rate a man might as well
go into a gravel-pit and count the pebbles and describe the colours.
How odd it is that anyone should not see that all observation
must be for or against some view if it is to be of any service!

-- Charles Darwin, in On the Origin of Species

The "view" Darwin cites above is the hypothesis.

A hypothesis is a "tentative proposition...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.
Hypotheses can be constructed and used in different ways.

    Overall Hypothesis

    When one first makes an observation that generates a question, that question can evolve into what is sometimes known as the overall hypothesis about the observation.

    For example, you might notice that in a population of wild goats

    • some males have curled horns
    • other males have straight horns
    • all females have straight horns

    This is your observation.


    You might wonder:
      "Does the shape of a male's horns affect his some aspect of his natural history?"

    This is your question.


    You pose multiple hypotheses to explain the variation in male horn shape:
    • "The shape of a male's horns affects his attractiveness to females."
    • "The shape of a male's horns affects his ability to fight other males."
    • "The shape of a male's horns affects his ability to ward off predators."
    • ("Insert your clever hypothesis here.")

    Any of these can be considered an overall hypothesis.


    Each of these could and should be tested.
    For now, we'll choose the first hypothesis: attractiveness to females.

    Experimental (Statistical) Hypotheses

    To make an overall hypothesis testable, it can be re-phrased as two mutually exclusive, experimental (statistical) hypotheses:
    • null
    • alternative


    Your observation of male goat horns led to a hypothesis that there is a relationship between two things: horn shape and attractiveness to females.

    To determine whether these two things are actually related, you must

    • design a rigorous experiment
    • collect data
    • statistically analyze your results
    • reject or fail to reject each of your experimental hypotheses


  • The null hypothesis states that there is no relationship between the two things:
      "Female goats are equally attracted male goats with straight horns and male goats with curly horns."

  • The alternative hypothesis states the opposite of the null hypothesis, that there is a relationship between the two things:
      "Female goats are not equally attracted to male goats with straight horns and male goats with curly horns."

    • The alternative hypothesis may be either
      • two-tailed (does not specify how the two things vary together)
        "Female goats are not equally attracted to curly-horned males and straight-horned males."

      • one-tailed (specifies a direction for the relationship)
        "Males with curly horns are [more/less] attractive to females than males with straight horns."

    The results of your experiment will indicate which of these hypotheses will be rejected, and which you will fail to reject.


Model Organisms Help Us Understand Biological Systems

A model organism is a non-human species used to study a particular biological phenomenon.

Model species are studied not because the investigator wishes to understand only how that species works, but because discoveries made in them may apply to the workings of other organisms, including humans.

Model organisms generally

Typical model organisms used in biological research include... ...among many others.

Remember: A model organism is a tool used to study a biological phenomenon that may be similar in other species.


Scientific Method Redux

Scientific progress proceeds differently from progress in other fields: A scientist
  • requires observable, physical evidence
  • devises experiments designed to falsify, not verify a hypothesis
  • has a willingness to modify or even reject long-held ideas that turn out to be wrong

    These set science apart from faith in things that cannot be observed or falsified.

    When you're ready, here's the Truth About the Scientific Method.

    References
    Platt, J. R., 1964, Strong inference. Science 146: 347-353.