Introduction to Cell & Molecular Biology (BIOL121) - Dr. S.G. Saupe (ssaupe@csbsju.edu); Biology Department, College of St. Benedict/St. John's University, Collegeville, MN 56321

The Nature of Science

I.  What is science?
    Latin - scientia, meaning to know or have knowledge.  By this definition, everything is science!

A.  Comparison of disciplines

Which is best?  None...each has its particular purpose and should not be used for the others. But, what is science?

B.  What science is.  Among the many possible definitions, Science�.  (don't memorize these)

    One of my favorite definitions is that �...all science is partly biological (George Gaylord Simpson - This View of Life)."   I like this quote because it highlights the importance of the observer in the scientific process.

C.  What science isn�t.   Science isn't involved in moral decisions, social attitudes, or beliefs.  But, clearly science and scientists are influenced by them.


II.  Goal of Science
    The goal of science is to understand the natural world and uncover underlying truths (facts).  Question:  what is a fact?  A fact is what most everyone agrees to be true.  Fact are real events that are/were observable.  Compare this to an inference � a deduction or conclusion drawn from facts that tend to be predictive, and do not describe real events that occurred.  

A.  "Problems" with facts.
    Just because everyone agrees, does that make a fact necessarily true?  Nope, they are only our best guesses at the time.  Why?  Simple � a fact is a fact only because we believe it. 

  1. Since facts are perceived with the instruments available to us, including our senses, who can say that our senses aren�t being fooled (i.e., optical illusions)?
     

  2. We know that there is both a physiological and psychological component to vision/seeing.  For example, individuals blind since birth and then able to see as the result of, say a cataract operation, have to be �taught� how to see.  Thus, seeing is in part a �learned experience.�  I believe that this even was the subject of a popular movie (Love at First Sight starring Val Kilmer, though I've never seen this film). 
     

  3. Perceptual blindness - psychologists tell us that people often don't "register" what they see.  In other words, they have "situational blindness."  Check out the great movies from the Simons lab at the University of Illinois.   One consequence of this work is to challenge the validity of eyewitness testimony. 

B.  Paradigms
    Facts are theory laden, encumbered by the prevailing ideas, or paradigms, of the day.   A paradigm is a way of looking at the world.  A paradigm is essentially your perspective or outlook on a situation.   It is difficult to escape and see past the current paradigms. Here's a simple example � not too long ago my wife and I attended the wedding of one of my former students, Tim. While sitting in church we saw some friends, Dennis and Betty, that we hadn't seen in quite awhile.  During the ceremony I wondered how they knew Tim since I didn't think they were associated with CSB/SJU.  After the ceremony we got chatting and it turns out that they were there for the bride, Kelly.  Duh!  Why hadn't I thought of that? I was blinded by my paradigm, "I was invited by Tim, so they must too".  The evidence was clear � Dennis and Betty were even sitting on the other side of the aisle in church �  but I just couldn't see past my preconceived paradigm.

    There are plenty of scientific examples of paradigms throughout the history of biology.  Two important ones include the explanation for the cause of disease and the origin and evolution of life.  The table below summarizes a few.

Table 1.  Paradigms Old and New

Idea

Old/Original Paradigm

New Paradigm

Cause of disease

diseases are caused by spirits (i.e., miasma theory -  diseases were caused/ transmitted by bad air, called a miasma. 

diseases are caused by germs (i.e., Pasteur & Germ Theory)

Origin and evolution of life

Creationism (God created life in the form we now find)

Evolution

Existing paradigms are always being challenged, tested.  Those that don't fit with the evidence are discarded as new ones are adopted.  Paradigms change over time and when they do it tends to be some of the most intellectually-stimulating times.  Two medically-related paradigms that have changed recently are: (a) the switch from a focus on disease (allopathic medicine) to wellness; and (b) recognition of some types of alternative medical therapies (i.e., herbal remedies)


III.  Philosophy of science  (not on exam)
    A few years ago, a supermarket tabloid reported that the actor/singer Tom Jones reportedly almost drown in a rip tide, but a large wave threw him back on shore.  What explanation can we suggest for his miraculous rescue?  Well, perhaps God grabbed him by his shorts and tossed him back onto the beach.  Alternately, a large wave just happened to roll in and catch him.  The first explanation is vitalistic - based on the notion that the universe is controlled by supernatural processes whereas the latter, is mechanistic.  This idea originated with the Greeks and essentially assumes that the universe is orderly and rational and is governed by predictable laws.  It should be no surprise that religions are vitalistic while science is mechanistic.   Thus, a scientist wanting to explain the Tom Jones �miracle� would look at it from a mechanistic perspective.  This allows the scientist to develop a hypothesis to explain the event and to collect evidence that would support or disprove this hypothesis.  For example, one hypothesis is that Tom was swept up in a beachward current.  However, there is no way a scientist could determine whether God actually helped.

    Related ideas are causalism and teleology.  According to teleology, events in nature occur according to a predetermined plan, in other words, everything has a time and purpose.  In contrast, causalists deny predetermination, but rather, suggest that events occur in a stepwise fashion, each event setting the stage for the next.  A good example would be in the novel Bridge Over San Luis Rey.  The author (Thornton Wilder) wrote from a teleological perspective following the lives of the individuals who died, suggesting that they were destined (�it was their destiny� - Darth Vader) to die on the bridge.  In contrast, a causalist would argue that it was simply chance that those five individuals happened to be on the bridge.  Perhaps their combined weight was sufficient to break the bridge after years of weathering.  Clearly, scientists view events from a causalistic perspective.

click here for more details

IV.  Types of Science

  1. Observational or descriptive - accurately describe nature (i.e., anatomy, astronomy)
     

  2. Systematic or taxonomic - naming and classifying organisms
     

  3. Model Building - a variation of experimental science in which models, usually mathematical computer models, are constructed and then tested to see if they fit the data. 
     

  4. Hypothetico-deductive or "Experimental" Approach - this includes the typical �scientific method� which usually includes a traditional hypothesis and a comparison of two groups (experimental vs. control) that differ by a single factor (variable).  A variable is any factor that could affect the outcome of the experiment.
     

V.  Hypotheses     

A.  Definitions.  A hypothesis is a tentative explanation for an observation.  It is your best guess.  For example, what if I think that Tom Jones was �saved� by a chance encounter with a large wave that pushed him into a beachward current. creativity, curiosity, extensive background knowledge are all helpful.  Pasteur is paraphrased as having said, "chance favors the prepared mind." 

B.  Criteria.  A good hypothesis must be:  

  1. testable - able to design an experiment or make an observation to determine its validity (I could test my hypothesis by studying currents in the area, the frequency and size of waves, whether it was high or low tide, etc.);

  2. falsifiable - the possibility exists that it could be false.  My hypothesis is falsifiable because it certainly could be potentially false.  For example, we could find after study that there are no beachward currents in the area.  Thus, we have shown it to be false, and must consider an alternative hypothesis; and 

  3. simpler than competing hypotheses (called Occam�s Razor).  As an example, I could propose that a dolphin happened to swim by, see Tom in trouble, swim underneath him then flick his tail and cast him up on shore - certainly less likely than our wave hypothesis.

C.  Stating Hypotheses
    Typically there may be more than one hypothesis to explain a particular event or observation.  These are listed H0...Hn.  For example, there are multiple explanations, though not all are equally likely, to explain what happened to Tom Jones.  These include:

H1 - Tom swam perpendicular out of the rip tide;
H2 - Tom was saved by a dolphin;
H3 - Tom had a surge of adrenalin and was able to swim against the current;
H4 - Tom was saved by a rogue wave that pushed him to shore; and so on.

What scientists essentially do is to disprove or eliminate all the alternative hypotheses leaving the one "correct" explanation.  Though in practice this sounds easy, it can be difficult to know when all potential hypotheses have been considered.

D.  The Nature of Proof
    You can never �prove� anything in science.  The reason for this is that impossible to test a given hypothesis under all possible conditions and circumstances.  Thus, we can never be absolutely certain that a hypothesis is True.   Consider the following "truth table":

Veracity (truth) of the hypothesis Predictions
True True
False True, False

If a hypothesis is true, then when we test the hypothesis, any predictions that are generated from the predication must also be true.  For example, let's consider the hypothesis "all green apples are sour."  If this hypothesis is true then we predict that every green apple we eat will be true.  Thus, the predictions from a true hypothesis will also be true.  However, if the hypothesis is false (which clearly it is), when we may still have true predictions (e.g., we eat a thousand green apples they might all taste sour) until we sample our first sweet "Granny Smith" apple.  Thus, a false hypothesis can give rise to both false AND true predictions.  What this means is that we can never prove a hypothesis to be true because true predications can be generated from both false and true hypotheses. 

Conversely, we CAN prove an idea to be wrong since it only takes one case to show that a hypothesis is not true.   To take advantage of the ability to disprove hypotheses, we often state them as a null hypothesis, H0, that is, we state that there is no difference between what we expected and what we found.  And, if we prove this to be false, then that means there must (�proved�) be a difference between the two.  This is a commonly used technique by �statisticians".  The take-home-lesson from this is that we can support an hypothesis, but we can't prove it. Don't say that you�ve �proved� your hypothesis �  instead, say that you've �supported� it.

VI.  Steps of the "Scientific Method"

  1. Make observations/gather information;
     

  2. Develop a hypothesis;
     

  3. Generate predictions from the hypothesis that will be tested with an experiment
         Predictions are generated by deductive logic - formulated as "If...then..." statements.  In deductive logic, we start with a generalization and make a specific conclusion.  Inductive logic is just the opposite, you start with a few specific observations and make a generalization;
     

  4. Design an experiment to test the hypothesis.  Don't forget a control group;
     

  5. Collect data (results) 
     

  6. Make conclusions;
     

  7. Report results;
     

  8. Repeat ad infinitum modifying hypotheses as necessary and/or generating new ones.


VII. Analyzing Experiments:  Some considerations

A.  The experiment must be repeatable
    To confirm conclusion and guard against one-time events (e.g., many examples in Secret Life of Plants).

B.  Avoid Bias in interpreting the Results
    Scientists are humans and subject to biases and prejudices.  Can minimize by doing double blind experiments and including placebos.  There are two major types of bias:

  1. Unintentional - unconscious slanting of data to get result (e.g., Pavlov assistant)

  2. Intentional - conscious manipulation of data = dishonesty!  Ranges from "prettying" up data (ignoring anomalous results) to outright fabrication.  Why would a scientist cheat?    Reasons:  competition for grant money; pressure to publish; glory.  Fraud is especially destructive in science since there is no time to check everyone's work, must assume it is correct.  It's hard to imagine why a scientist would cheat since the self-corrective nature of science will likely expose the cheater.

VIII.  Biology has few laws  
    The more data that support a hypothesis, the more strength that it has, and we presume the greater "truth".  The general scheme of increasing strength of support for an idea is: hypothesis → theory  → law.  In practice, there is no clear distinction between law, theory and hypothesis.  For example, even though you hear of the �theory� of evolution, it doesn�t mean that biologists consider it is any less a fact that say, the �law� of gravity.

    Thus, biology is a statistical science.  Generalizations in biology are probabilistic - because there are often exceptions.  Why?  because living organisms are so diverse and exhibit exceptional variety.             


 VIII.  A Case Study:  Ignaz Phillip Semmelweis and Childbed Fever (for more information see the article by Brown and Williams, 1990) (not on exam)

  1. Background & Problem: 
        Semmelweis (1818-1865) was a Hungarian physician; received a medical degree and midwifery from the University of Vienna (1844).  He worked at Vienna General Hospital Obstetrical Clinic.  He noticed that many women in the clinics died of childbed fever.  The cause was unknown but was "hypothesized" to be caused by miasma.  Because of the high risk of dying from this fever which was associated with hospitals, most women had births at home.  Clinics were just for poor, unwed mothers.  Childbed fever is now known to be caused by Streptococcus pyogenes (Staphylococcus) - like one that killed Jim Henson (Mr Muppet) and the face-eating bacteria.

  2. Observations:
        There were two clinics at the hospital � one with medical students and physicians and the other with midwives.  The mortality rate was high in the First clinic that was tended by physicians and students.  The mortality was low in the Second clinic staffed by midwives.  Semmelweis observed that in the first clinic the physicians and students would perform autopsies, exams, and examine sick patients.  The midwifes cared for patients but performed no autopsies.  The mortality in the First clinic was high (about 10%) but low in the Second (about 2%).  The First clinic admitted patients on Saturday, Sunday, Tuesday and Thursday, while the midwife (Second) clinic admitted patients on Monday, Wednesday and Friday.  The delivery practices in the two clinics were the same and the deaths occurred in patients admitted on Saturday, Sunday, Tuesday and Thursday.  One of Semmelweis teachers died from infection received during autopsy; symptoms similar to childbed fever.

  3. Hypothesis:  
            The medical students and teachers are passing the disease from patient to patient

  4. Alternate hypotheses:  
            The patients sick before arrival.
        Miasma caused disease.   

  5. Predictions:  
            IF the medical students and teachers are passing the disease from patient to patient THEN, washing hands should prevent disease

  6. Experiment: 
        Semmelweis ordered everyone to wash hands with chloride of lime

  7. Result:  
        Death rate dropped.  

  8. Conclusion/Evaluation:  
    Semmelweis - Problem solved
    Others - Coincidence (especially because they stopped washing hands and death rate rose again.  End result:  Semmelweis' work NOT accepted especially because he didn't publish his work.  He spoke in 1850 at Med. Society of Vienna and physicians were on the verge of acceptance, but adidn't because he hadn't published (at least until much later in a rambling incoherent way).  He eventually died in an insane asylum at 47.  

                 For a dialog about Semmelweis, click here

Take-Home-Lessons:

  1. Societal acceptance of/impact on science

  2. Development of hypothesis; tested by gathering evidence

  3. Control vs. experimental groups - indirectly

  4. Importance of publication

  5. Standard scientific method not always - usually have an opinion about what the endproduct of experiment is...do experiment to support/prove yourself wrong.

  6. The time was not ripe for understanding of his discoveries

IX.  Case Study - Frog and Toad in the Garden  (not on exam)
    This wonderful children's story by Arnold Lobel provides a look at experimental design and inappropriate conclusions (non-causal correlations) based upon a poorly designed (no control group) experiment.
            click here for details
 

X.  Case Study � Card Playing (Science is Predictive)  (not on exam)
    We will see if you can determine the pattern that I arrange in a deck of playing cards.  This exercise should focus on concepts of falsifiability, certainty, proof, alternate hypotheses, laws and biology.              
       click here for a mini-exercise
 

XI.  Case Study - Dodos and Trees  (not on exam)
    This story is included because it demonstrates nicely how scientists can study past events. It is also a good example of non-causal correlations.  

A.  Observations
    On the island of Mauritius, the native home of the now extinct dodo, Calvaria trees have not germinated naturally for about 300 years (the age of the youngest tree), which is about the same time as the dodo went extinct.

B.  Question
    Was the extinction of the dodo responsible for the failure of the Calvaria tree seeds to germinate?

C.  Hypothesis
    Germination of the seeds of the Calvaria tree only occurred naturally after they passed through the gut of a dodo.

D.  Collect evidence/experiments.
    We haven�t mentioned it yet, but technically what scientists do to support or disprove a hypothesis is to determine if predictions that arise from their hypothesis are true.  Let�s list some predictions from the hypothesis.  IF the dodo is responsible for the failure of the Calvaria seeds to germinate, THEN......

  1. there should be no trees younger than 300 years when the dodo went extinct.  (This is true)

  2. the dodo should have been big enough to eat the seeds (True)

  3. the dodo was small enough to prevent completely crushing the seeds (True, demonstrated by inference to other bird studies);

  4. dodos would eat seeds (True - seeds found in fossils)

  5. passage through a dodo gut should cause the seeds to germinate (True - from studies force-feeding turkeys).

E.  Conclusion
    The author concluded that the extinction of the dodo has lead to the near extinction of Calvaria trees because the seeds have a very hard pit that can not be broken down.  The evidence seems to be very strong in support of the hypothesis. 

    But, does this prove our hypothesis?  No for at least two reasons: 

  1. Remember we can never �PROVE� our hypothesis - it�s always with the realm of possibility that an alternative is true - for example, perhaps the seeds aren't germinating because of a climate change, or a new disease, or pollution in the water, or deforestation, or introduced species, etc.  In fact, this article, which was published in one of the premier journals of American science, stimulated a flurry of research and now has been discredited.  
     

  2. The experiment was poorly designed/executed [small sample size, inadequate controls, inadequate background information (small trees ARE known), inappropriate graph interpolation]

For more, check out David Hershey's essay, "The Widespread Misconception that the Tambalacoque or Calvaria Tree Absolutely Required the Dodo Bird for its Seeds to Germinate" in Plant Science Bulletin 50: 105 (2004).

Dodo Article Analysis - click here


XII.  Case Study - van Helmont and his willow (not on exam)
    We will analyze this classic experiment that was very important in the history of plant physiology.  Interestingly, it had some �flaws�. 
                 Click here for details.

    
XIII.  Case Study - Willow Cuttings (not on exam)
                 Click here for details.
 

XIV.  Case Study - White Snakeroot  (not on exam)
      This study comes from an article by D Duffy, Land of Milk and Poison published in Natural History magazine, July 1990. 
                Click here for details

XV.  Case Study - Turtles  (not on exam)


References:

 

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Last updated: July 14, 2009     � Copyright by SG Saupe