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    Solving Problems in Biology

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

    Scientists use both

    • inductive reasoning
    • deductive reasoning

      ...to address biological questions.

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    Inductive Reasoning

    Induction involves using many individual observations to make a generalization.
    This approach moves "from the bottom up".

    • Item X has characteristic A.
    • Item Y has characteristic A.
    • Item Z has characteristic A.

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

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    The Risk of the Inductive Leap

    One could make many individual observations:

    • This bee stung me. It belongs to taxonomic group Hymenoptera.
    • This hornet stung me. It belongs to taxonomic group Hymenoptera.
    • This fire ant stung me. It belongs to taxonomic group Hymenoptera.

    And come to a general conclusion:

    • Therefore, all Hymenoptera have stingers.


    You might already see the potential pitfall here.

    A general conclusion derived 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 fact, many hymenopterans lack stingers.

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Generalizations can certainly be useful,
but there may be exceptions to a general rule.

A "general rule" might even (eventually) turn out to be wrong most of the time.

Deductive reasoning precludes this complication.

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    Deductive Reasoning

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

    • Everything in Class X has characteristic A.

    • This thing in my hand belongs to Class X.

    • Therefore, this thing in my hand has characteristic A.

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    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:

      1. Inductive Generalization: "All items of type X have characteristic A."

      2. Observation: "This thing in my hand is of type X."

      3. Specific Conclusion: "Therefore, this thing in my hand has characteristic A."


    Example:

      1. Inductive Generalization: "All hornets have stingers."

      2. Observation: "This thing in my hand is a hornet."

      3. Specific Conclusion: "Therefore, this thing in my hand has a stinger."

    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.

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    Hypothesis, Theory, and Law

    Science requires
    • falsifiable hypotheses and theories
    • rigorous experimental testing of hypotheses
    • observable evidence

    The word "theory" is often used colloquially
    to describe a hunch or "gut feeling" based on speculation.

    However, the scientific definitions of

    • hypothesis
    • theory
    • law
      ...are more precise.

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    Hypothesis

    A scientific hypothesis
    • is a tentative explanation for an observed phenomenon.
    • must be experimentally testable
    • cannot be proven to be true

    Example: Feather pigmentation in flamingos is influenced by carotenoids in their diet.

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    Theory

    A scientific theory
    • is a well-substantiated explanation of some aspect of the natural world
    • can be used to make predictions
    • is subject to testing, revision, and refutation

    Example: Evolution proceeds by means of natural selection. (Darwin's Theory)

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    Law

    A natural law
    • describes events in nature that occur exactly the same way,
      every time, under the same conditions
    • is dependent upon cause and effect
    • predicts a specific result under a specific set of circumstances
    • does not explain why that result occurs

    Examples: Laws of Thermodynamics

    • Energy cannot be created or destroyed, but only changed in form.
    • A closed system will to move towards a state of greater disorder/chaos.

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    Science as Falsification

    You have a hypothesis about how something works.

    • Should you seek to prove that your hypothesis is true?

    • Or should you seek to disprove your hypothesis?

    The correct answer may come as a surprise.

    <-- Watch the video. Then we'll talk.

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    Popperian Science: Falsifiability

    Falsifiability (refutability) is the capacity for a hypothesis
    to be contradicted by evidence.

    In his essay Science as Falsification,
    Karl Popper explained why a powerful hypothesis

    • is NOT made stronger by repeated verification.
    • must be vulnerable to falsification.

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    Falsifiability: Keystone of Popperian Science

    A hypothesis

    • must be testable in such a way that it could be falsified.

    • is not scientific if it cannot be falsified.

    Experiments must be designed to rule out hypotheses that are clearly wrong.

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    Falsification entails a process of elimination.

      1. Make an observation about something in the natural world.

      2. Pose competing hypotheses, each of which could potentially explain the observation.

      3. Design rigorous experiments to test each hypothesis.

      4. Predict results that would either support or refute each hypothesis.

      5. Analyze experimental results.

      6. Reject or fail to reject the hypothesis being tested.

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    A Hypothesis Cannot be Proven True

    In Popperian science, a hypothesis
    • can ONLY be either (1) rejected or (2) not rejected.
    • can NEVER be proven to be true.

    A hypothesis NOT falsified by experimental results
    • can be provisionally accepted as a potential explanation of the observation.
    • IS NOT NECESSARILY TRUE.

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    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 the hypothesis "There are no fish in this new body of water" is TRUE?


  • None of the many trials has refuted the "no fish" hypothesis.

  • The "no fish" hypothesis can be provisionally accepted. For now.

  • But a future capture of even ONE FISH would indisputably refute the "no fish" hypothesis.


  • The explorer has NOT proven the "no fish" hypothesis to be true.
  • Rather, he has failed to prove the "no fish" hypothesis to be untrue.

    This is a subtle, but critical distinction.

    The explorer must remain open to the possibility
    that his (currently) unrefuted hypothesis might be refuted in the future.

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    Future Experimentation

    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.

    The "no fish" hypothesis could be refuted.

    As technology improves, science marches on.

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    A Hypothesis Can Be Likened to a Fortress.

    "This fortress is well built and perfectly sound."
    (But how do you know for sure?)

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

      1. List the ways the fortress is built to be strong.
      2. Attack the fortress and try to break it down.

    • Listing fortifications = listing evidence that supports the hypothesis.
    • Attacking the fortress = doing an experiment that could refute the hypothesis.

    If the hypothesis is incorrect, then rigorous attack will reveal the truth.

    No matter how well built the fortress might be,
    you don't know how strong it is until you actually
    try to break it down.

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Strong Inference: Competing Hypotheses

John R. Platt coined the term strong inference to describe a straightforward,
powerful method of addressing a scientific problem:

Pose multiple, competing hypotheses, any of which could potentially explain an observation.

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    The Essence of Strong Inference

    1. Devise alternative/competing hypotheses that potentially explain an observation

    2. Devise an experiment

    • with two alternative possible outcomes.
    • with each outcome able to eliminate one of the two hypotheses.

    3. Perform the experiment cleanly and rigorously.

    4. Analyze results and eliminate one of the two competing hypotheses.

    5. Use the results to devise new alternative, competing hypotheses.

    6. Repeat this the procedure with sequential hypotheses to refine the remaining possibilities.


    Platt likened this to climbing a tree with a series of
    dichotomous (two-way) branches.

    At each branch, the experiment determines whether to choose
    the right or left branch.

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    Strong Inference is like analysis
    of an unknown chemistry sample.

    Add reagent A to the unknown.

      1. If you get a red precipitate, it is subgroup α
        Add reagent B
          1a. If you get a black precipitate, it is subgroup β
          1b. If you get no precipitate, it is not subgroup β
        ...and so on.

      2. If you do not get a red precipitate, is it not subgroup α

        Add reagent C
          2a. If you get a white precipitate, it is subgroup γ
          2b. If you get no precipitate, it is not subgroup γ
        ...and so on.

    (Find the full text of Platt's paper HERE.)

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    Strong Inference: Let House Demonstrate

    The Differential Diagnosis scene in every prologue of a "House" episode
    is a cartoonish 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?


    Confirmation bias is the tendency to interpret evidence
    as confirmation of one's own pre-existing ideas about an observation.

    Posing only one hypothesis to explain an observation can lead to confirmation bias.

    Competing hypotheses can help prevent confirmation bias.

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



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Hypotheses

The hypothesis is the cornerstone of science.

Hypotheses can be constructed and used in different ways.



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    Overall Hypothesis

    A question inspired by an observation can evolve into an overall hypothesis.

    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 compete with 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.

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    Experimental (Statistical) Hypotheses

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

    • Null Hypothesis (H0)
    • Alternative Hypothesis (HA)


    We have hypothesized that there is a relationship between:
    • male horn shape
    • male attractiveness to females

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

    • design a rigorous experiment
    • collect data
    • statistically analyze your results
    • reject or fail to reject (H0)
    • reject or fail to reject (HA)


  • The Null Hypothesis (H0) states that there is no relationship between the two things:
      "Males with curly horns and males with straight horns
      are equally attractive to females."

  • The Alternative Hypothesis (HA) states the opposite of the null hypothesis:
    that there is a relationship between the two things:
      "Males with curly horns and males with straight horns
      are not equally attractive to females."


    Our experimental results will indicate which of these hypotheses
    will be rejected, and which will fail to be rejected.

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    But Wait, There's More.

    An Alternative Hypothesis (HA) may be either

    • two-tailed (does not specify how the two things vary together)
      "Males with curly horns and males with straight horns
      are not equally attractive to females."

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

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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 a model system 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.

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Scientific Method Redux

Scientific progress proceeds differently from progress in other fields: A scientist

These set SCIENCE apart from other disciplines that do not rely on observable evidence.

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


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

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