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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.

Model Organisms Help Us Understand Biological Systems

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

• are easy to raise and maintain
• have a short generation time
• are used when human (or other species) experimentation would be unfeasible or unethical

Typical model organisms used in biological research include...

Various species of bacteria Escherichia coli is usually the star of the show, but many other species contribute to our genetic knowledge. One obvious advantage is their extremely rapid rate of reproduction.

Yeast Saccharomyces cereviseae and other species.

Thale Cress Arabidopsis thaliana is considered the "fruit fly of the plant kingdom". It is used extensively in studies of plant biological systems.

Drosophila melanogaster Its name means "black bellied sugar lover." But you will meet many mutant forms with colors and shapes to amaze you.

Danio rerio Zebra fish are commonly used to study the biology of development, including neurological development genetic defects in development evolutionary development (EvoDevo)

House Mouse The genome of Mus musculus is oddly compatible with that of Homo sapiens, and for this reason it makes an almost ideal model mammal for genetic studies.

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

• When you use onions to study mitosis, you're investigating mitosis, not onions.
• When you use toxins to modify mitosis, you're investigating mitosis, not toxins.
• When you use yeast to study enzymes, you're investigating enzymes, not yeast.

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Scientific Method Redux

• observation - The investigator notes a phenomenon that poses a problem/elicits a question.
• hypothesis formulation - The investigator poses multiple, competing hypotheses that could potentially explain the observation.
• prediction - The investigator makes a statement about what s/he believes will happen when each hypothesis is put to the test.
• experimental design - The investigator designs an experiment to generate data that will allow the investigator to either reject or fail to reject each hypothesis.
• data collection - Experiments are run, data are collected.
• data analysis - 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 fails to refute the hypothesis.

• 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 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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