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Background theory
http://faculty.fmcc.suny.edu/mcdarby/Majors101Book/Chapter_01-The_Basics/02-Scientific_Method.htm





 
 
 

 
 

  
 
 
 

 

 
 

 We accept today that science follows certain rules and processes that make it a dependable source of information, but those rules have not always been in place.  Until as recently as the 1600s, for instance, it was widely believed that living things could arise spontaneously from non-living, dead, or waste materials (this is called spontaneous generation), because people saw such materials "generate" living things such as mold or maggots.   In 1688, Italian naturalist Francisco Redi set out to test the idea with decaying meat in two containers:  one open to the air, the other sealed.  The open container meat eventually became infested with maggots.  And when critics insisted that it was the sealing of the second container that kept spontaneous generation from occurring, Redi did the test with an open container and one covered with cheesecloth, through which air could circulate (he suspected what we know, that flies were the actual source of the maggots), and the cheesecloth-covered sample produced no maggots.  However, even as certain aspects of spontaneous generation became recognized as wrong, when germs were first discovered it was first thought that they were a spontaneous product of sick tissues, rather than independent-living organisms that reproduced in the body.
It was a long road from that basic test to today's scientific method (discussed in Subsite 1, Section 2 and reviewed here), but some of the approach Redi used persists:  modern science is about testing suspected explanations of one's observations, which can be made directly through one's own personal senses or indirectly through instruments or second-hand from someone else's direct observations.  An explanation for one or more observations is properly called a hypothesis.  A hypothesis should produce testable predictions or it isn't much use scientifically, and the tests are most reliably done under controlled conditions.
In biology, complete control over conditions is hard to achieve, but scientists still strive for it.  If no alternative exists, testing may be done in the field, with well-planned and organized series of observations that look for evidence for the hypothesis' predictions.  Controlled experiments may be done in a laboratory environment with different test groups, similar to how Redi did his experiment.  One group, the experimental group, is specifically set up to test some critical aspect (the variable) of the hypothesis;  another group, the control group, duplicates the experimental group but removes the variable (or, if that isn't possible, changes it in some significant way).  In Redi's second test, the experimental group was the cloth-covered containers (the cloth barrier as a test of air access but fly blockage was the variable), with the control test being containers with no cloth over them.  
Results, usually in some sort of number form (quantitative data, as oppose to non-number qualitative data) are collected from each group and compared.  The comparison is absolutely critical - just running an experimental group is possible (we could give a new headache remedy to a group of 100 people with headaches and record how much their headaches improved), but how would you know whether your results were directly connected to your variable - how many headaches would have improved on their own, or improved just because the subjects were given a pill and expected improvement (improvement based solely on expectations is called the placebo effect, placebo being an "empty" treatment)?  In a proper experiment, a control group would have been treated identically, given pills with the remedy ingredient removed;  the difference in effects in the two groups can be said to be an effect of the remedy itself.
 
Modern science is based upon a descendant of that original scientific method, with some additions and minor changes.  A good experiment should be clearly designed and stated, and reproducible, so that someone else running the same test will get approximately the same results.  Research also generally is subject to peer review, scrutiny by others in the same field, usually when results are being published (in peer-reviewed journals) but sometimes at other stages of the process.  Peer review can be a double-edged sword:  on the one hand, it should help to assure that research is being properly done and conclusions make sense, but on the other hand, often established scientists can be resistant to truly innovative ideas and approaches.
Modern biology, including medical research, can be confusing for a number of reasons, especially for the general public.  Often different studies seem to be completely at odds with one another, when in reality they were not looking at the same thing, or the results were misinterpreted by the media.  How data is collected can affect results (how would the headache study above be influenced if the rating system went from "1 = barely there, to 10 = the worst headache you could imagine"?), and experiments with living organisms are affected by a wide range of confounding factors, things that might be influencing the results.  One of the most  common confounding factors is pure chance -  if the mouse you've picked to test happens to be particularly prone to cancer, anything you test will look cancerous - which requires that, whenever possible, test groups must be of sufficient size.  If you use 100 mice, that one cancer-prone one will not significantly affect your averaged results.  Conclusions based on a single instance or a very limited group are said to be based upon anecdotal evidence and are not considered to be reliable.  You know the basic logic here:  just because you were lucky enough to get away with something once doesn't mean you'll always be able to get away with it.
Obviously, if a test subject knew they were receiving a placebo, that would influence their responses;  this is why they are not told, producing what is called a blind test.  It was determined decades ago, however, that if the people giving out the treatments themselves knew which were real and which were placebos, they tended to treat the patients differently, sending subtle messages that might alter patient responses and results.  To eliminate those confounding factors, modern drug tests are double-blind:  those giving the treatments deal with numbered samples packaged and recorded elsewhere, not knowing which are real and which are not - there's no way they can alert the patients, even unconsciously, if they don't know which dose is which.
A researcher tries to recognize potential confounding factors while designing an experiment, and either eliminate them or set up separate control tests to determine their influence, but researchers can't anticipate everything.  Often peer review will reveal a possible confounding factor never recognized, and it's back to running the test again.
  
 
 
 
 
 
 
  
 
 
  
 
 

 

 
  
 

 A discussion about making qualitative data - ancient texts - quantitative for comparison purposes.
A page on experiment design for amateur scientists.  It's a bit strangely set-up but still easy to navigate.
A technical page on designing "microarray" experiments - just notice the basic needs addressed and don't sweat the nasty details, and you'll see the same foundations of experiments discussed here.
A short list of possible confounding factors in physics experiments (but which apply to many biology experiments as well.
A study comparing placebos:  fake pills against fake acupuncture.
A blog about homeopathy trials that does a nice job explaining the requirements of medical testing.
An article with a historical perspective on how basic science works - better to be wrong than to let somebody fake your evidence.
An wide-ranging web page  on the subject of peer review of articles published just on the internet.
An information page about how peer review works on grants for federal government money in the health fields.
A fairly bizarre page on research done with marshmallow peeps that sort of follows scientific method but uses groups that are too small to eliminate chance as a confounding factor.
Bread is dangerous!!

WHAT MAKES BIOLOGY
"SCIENTIFIC"?

  
 
 

Science as a Study of How the World Works

  
 

 Human beings are not completely happy as a group unless we feel that the things that happen to us and the world around us can be understood and explained.  As humans and human society have evolved, the sophistication level of acceptable explanations has had to keep pace:  from what is now known about the Earth, it's harder to convince people that the world is on the back of a giant turtle, or that everything in the universe circles it, or that it has only been around for a few thousand years.  
Explanations can be cultural, religion-based, or scientific, but make no mistake, there are no clear lines separating these - each is strongly influenced by aspects of the other.  The job of a book like this is to try to make clear what a "scientific" approach is, but no human researcher in the world is totally unaffected by the culture they were raised in or later exposed to, or the religions they follow (or reject).
One aspect of the philosophy of Post-Modernism is the idea that every choice we make is based on a personal world-view that affects what we are willing to perceive in the world.  The statement, "I wouldn't have believed it if I hadn't seen it," has a flip side, stated by Ashleigh Brilliant as,  "I wouldn't have seen it if I hadn't believed it."  Scientists like to believe that they are above such earthly influences, but the best scientists like to step back and look at their own biases whenever they make a conclusion, to try to actively detach themselves from their own worldview.  Do you think that is that even possible?
Having acknowledged the overlap of influences, it is possible to detail what is accepted as a scientific approach to explaining the world:  science is an approach that insists upon a fairly rigid structure, used to answer simple questions with controlled procedures designed to weed out answers that also appear but may not answer the question.  Confused yet?  Science is hard to reduce to a short definition;  the best way to explain it is to show you how it is supposed to work.
  
 
 

SCIENTIFIC METHOD AS A WAY TO ANSWER IMPORTANT QUESTIONS

  
 

 Like a lot of processes with rigid rules, when done by humans scientific method runs into some application problems.  These problems will be discussed in kind of a Point / Counter-Point fashion throughout this section.
  
 
 

SCIENTIFIC METHOD - OBSERVATION

  
 

 The beginning of the Method is like any approach to explaining how the world works:  it's based on first observing what's happening in it.  An observation can be direct, made using your own personal senses, or indirect, using someone else's senses second-hand or using technology to detect features of the world that human senses can't.  Occasionally an observation can be the results of someone else's experiments when you disagree with their conclusions.  To be scientifically useful, an observation should require some sort of explanation - "Your pants are blue" could lead to some science, but that seems less likely than,  "Every plant I've seen is green."  Why are they always green?
As mentioned earlier, though, what we observe is based at least somewhat on what we expect:  one person's floating log is another's lake monster.  There are assumptions embedded in our observations that can greatly affect both what we sense and the sense we make of it.
  
 
 

SCIENTIFIC METHOD - HYPOTHESIS

  
 

 We have been working from observing the world to explaining the world.  In Scientific Method, such an explanation is called a hypothesis, and there are a couple of requirements for a legitimate hypothesis.
First, a hypothesis should be predictive:  its should be clear enough that someone should be able to say,  "Well, if that explanation is right, then this should happen under these particular conditions."  Say you decide that plants are green because they all share some green chemical critical to their survival.  That would mean that removing the green chemical should kill a plant.  It would also mean that you shouldn't be able to find plants in the world that are not green (of course, to do this "being green" couldn't be used to define what a plant is!).

Second, a hypothesis should be testable:  you ought to be able to check to see if things work the way the predictions expect them to.
In real-world science, some popular ideas can fit the first criterion only, although it helps to be able to imagine some tests.  Many of Albert Einstein's theories about the nature of light, space, and gravity were such that tests could only be imagined, but they were still widely accepted long before technological developments allowed them to really be tested.
Scientific Method is based upon Logic.  This sounds obvious and a fairly easy rule to follow, but a hypothesis that seems logical to one person may not to another, and an "obvious" prediction may only seem obvious to the predictor.  This is especially true in biology, where "If A exists and B exists, then C should happen" are often based upon an incomplete understanding of A, B, and C.  Biology is full of results that amount to, "We checked for C, and we don't think we've found it - in fact, we're not sure what we've found."   Of course, that won't stop the concept of Application of Logic from showing up throughout the rest of the Method.
Hypothesis revisited.  When the testing phase has been done, a researcher looks at the results and decides whether the predictions were supported, but even this decision is a hypothesis.  People like to speak of science as being able to "prove" things, but all it can really do is collect evidence to support hypotheses.  For a very long time, test after test accumulated evidence to support the idea that gravity was an attractive force, and then Einstein came along and described it as a property that bent space, which is now the accepted explanation.  It is the strength of science that no idea is ever absolutely confirmed.  New ideas overturn and replace old ones all the time, or adjust how old ones are viewed.  In the media and fiction, a scientist's unwillingness to speak in absolute certainty is sometimes shown as a weakness, but it shouldn't be.  It's an easy trap - even folks who should know better will often use the "p-word."  (That's prove, folks.)
  
 
 

SCIENTIFIC METHOD - TESTING A HYPOTHESIS WITH CONTINUED OBSERVATIONS

  
 

 Testing a hypothesis means testing the predictions based upon the hypothesis.  The classic Scientific Method test is the controlled experiment, but that will be treated a little bit later.
In many instances where controlled experiments are not possible, or at least not practical, predictions are tested by follow-up observations, also called field tests.   The prediction that one should not be able to find non-green plants is really only testable this way - you go out looking extensively in the environment.
This type of test, and experiments as well, have some basic requirements within the Method.  First, it is useful if not essential to try not to test too many things simultaneously - if your hypothesis about plants was that they share a green chemical that is necessary to their life because it allows them to process sunlight but wouldn't work without a water source, that's a valid hypothesis but difficult to test all at once.  It would be easier to make and test predictions based on parts of the hypothesis, fitting the pieces together as the testing phase progresses.  Second, any questionable terms should be defined - if greenness isn't going to define plants in your study, then how will you decide what is a plant?  
A part of good science is that tests should be reproducible by other testers - they need to know how you did the test, and the first part of understanding that is knowing how you defined your terms.  If you are unclear on how you decided a plant is a plant in your study, someone else could do the test with their own assumed definition and include fungi (they used to be considered as types of plants, so that's not very far-fetched) while yours did not, and they will be running a very different test than yours.   Part of making a test reproducible  is designing it clearly enough that if someone else runs it, they will get comparable results.   The clearer your methods and descriptions, the better - what are you looking for, and how are you going to look?  If measurements are involved, exactly what will you be measuring and how?  When will you be done looking, and then what are you planning to do with the data you've collected?  "Let's look for some stuff, and then do some stuff with it" is not a good plan.
Most scientists prefer measurements to consist of quantitative data rather than qualitative data.  Quantitative data involves quantities, or discrete numbers;  qualitative data involve quality, based more on feelings or opinions.  In a test of headache medication, it is more useful to have subjects rank their pain, say, on a scale of 1 to 10 (Scale definition is still important - which end has more pain?  Is 10 the worst headache the subject has ever had or the worst headache they could imagine?).  Pain is qualitative, it doesn't have numbered notches, but it will be easier to track the effects of the drugs if you have before-and-after numbers to compare.  In a case like this, the numbers are not really comparable - one person's "3" might be nothing like another's - but if we're looking for the numbers to go down as an indication of the drug working,  we have a legitimate measure of effectiveness.
  
 
 

SCIENTIFIC METHOD - TESTING A HYPOTHESIS WITH A CONTROLLED EXPERIMENT

  
 

 People imagine scientists in lab coats, holding test tubes and scribbling furiously in notebooks, doing experiments, and many scientists do work under conditions like that.  But it's not the environment that makes the experiment, it's the Method.
Like a system of follow-up observations, and experiment should test a prediction based upon a hypothesis, and again, the more limited and specific the prediction, the clearer the results.  It will be necessary as well to set up tests that someone else should be able to reproduce, so clearly stating the terms and methods, understanding ahead of time how measurements will be made and data recorded, and how the data might be analyzed are all aspects of good science.  Often medical experiments reported on in the media seem to be contradictory (and sometimes they are), but often the studies were answering similar questions in different ways.  For instance, two studies on the effectiveness of long-term treatment of heart attack patients seemed to have conflicting results, but close inspection would reveal that one study looked at recurrence of heart attacks, which dropped, and the other looked at mortality, which remained the same.  From the two tests together, the treatments seem to lessen a person's chances of having another heart attack without changing their likelihood of dying.
So a test is designed.  By classic Scientific Method, what is hoped for is an experiment that allows for a control test:  a duplicate of the experimental test, with allowances made confounding factors, aspects that are not what you are testing but which might be changing your results.  At its simplest, all confounding factors can be accounted for by isolating what exactly is being tested as a variable.  In a test for headache medication, the medication itself would be the variable:  the experimental test would involve giving the medication to people with headaches (how would you measure the effectiveness of such a drug?), while the control test would involve giving equivalent treatments but with the medication removed.  If the only difference between the two tests is exactly what is being tested, in this case the medication, any differences in the results of the tests can be linked to just the medication.  Any other differences confuses the issue:  if you gave your experimental subjects pills but your controls nothing, how could you tell if just the act of being given a pill might affect a headache?  Humans do react to just the act of being treated - this reaction is called the placebo effect  (a placebo is a false treatment).
Usually in biological research, a group of organisms or some other complex system has something done to it, and a specific type of response is looked for.  For instance, a test could be set up to extract the green chemical from a group of plants and then check their growth (Terms! - What would you measure about a plant to determine growth?  How many different ways could "growth" be measured?) for a period of time.  Aha!  They all not only stopped growing, but by the end of your study they were all dead!  That green chemical must be critical for their growth and survival.
Or is it?  Experiments can be full of confounding factors.  One common type of confounding factor is called an artifact:  this is some aspect of the study process itself that produces results independent of what we are testing.  In the case of our plants, the treatment we used to extract the green might have killed them itself.  It might be impossible to ever determine whether the death of our plants wasn't poisoning rather than lack of green.
Many experiments cannot be designed with a clear-cut control;  our green chemical test could be controlled if we could magically remove the green and disturb no other aspect of the plant, but that can't be done.  The best that can be done is test the various parts of the extraction process and try to figure how much a plant is hurt by them.
Designing a good experiment is as much an art as a science.  It requires imagination, because many confounding factors are not immediately obvious, and trying to control for them may be challenging.  Often there is a level of guesswork and cost-benefit analysis - you may decide that one factor's impact would be too small to worry about, or another has to be accepted and worked with in the conclusion stage because actually dealing with it would be impractical (many experiments on humans would be more reliable if subjects could be locked away with every bit of their lives controlled, but researchers know they cannot do this).  It is almost a given that, after all is done, someone looking to criticize your work can come up with a valid confounding factor that you either didn't anticipate or that you had accepted but dealt with.
  
 
 

SOME EXAMPLES OF CONFOUNDING FACTORS

  
 

 Testing involving mice once used undisturbed mice in the same laboratories as controls, until someone realized that if the experimental mice were being pulled from their enclosures and having things done to them in the experimental test, then the control tests should duplicate those parts of the procedures too.  So today, if the experimental group is being given a 1-milliliter injection containing a test substance, the controls will be given a 1-milliter injection lacking the test substance.
You may hear that aluminum pots and utensils can give people Alzheimer's disease.  This goes back to studies that found aluminum residue in the brain tissue of Alzheimer's patients, residue that was not present in controls' (people without Alzheimer's) tissue.  But what turned out to be happening is that something about the chemistry of Alzheimer's tissue - the disease definitely changes brain chemistry - pulled aluminum out of the preserving fluid, while normal brain tissue did not - the aluminum had not been there until the tissues had been removed from corpses and preserved!
Studies involving groups of humans are notoriously difficult to control.  How do you match exactly two groups of people for everything except one variable?  For instance, a hypothesis linking Sudden Infant Death Syndrome (SIDS) to the cultural practice of sleeping with infants compared the U.S., where parents rarely sleep with infants, to an African nation where the practice is common, and found the SIDS rate much higher in the U.S.  One important confounding factor involves the data used, which were cause-of-death statistics:  it seems like SIDS is rarely considered as a cause-of-death by medical personnel in the African nation, even when no clear cause is present, so it's very unlikely to be recorded as such.  The SIDS differences could be just a reflection of this artifact.
Human expectations can often be a confounding factor, and sometimes expectations are not even controlled for because everyone accepts them.  It is widely reported, and accepted in several scientific areas, that crack cocaine has profound effects on the behavior of infants, so-called "crack babies."  This has become an acceptable premise, an assumption when looking for effects on their later development, but no one had really tested the idea that crack babies really are different from regular ones.  Finally, someone tested the premise:  nurses and caregivers in a hospital nursery unit were given the task of sorting the crack babies from the regular ones without knowing ahead of time which were born to addicts.  The hospital personnel were sure they would be able to do this, were certain that the behavioral differences would make it easy, but were no better able to choose which were which than a control flipping a coin (often, controls involving choice are not a classic control test, but a way of choosing randomly;  a real choice should not duplicate a random one).
The placebo effect was recognized fairly quickly when research into medical treatments became integrated with the Scientific Method, and it was quickly decided that patients should have no knowledge of whether they were in a study group or a control group - they are informed that they are in a case-control test, but are not told anything beyond that.  These were blind studies, sometimes called single-blind tests.  It was much later that the hypothesis arose that the researchers administering the treatments, knowing who was in which group, might treat the patients in each group differently.  Would your attitude be different giving an experimental treatment to one patient and a false treatment to another?  Even when the researchers tried to control themselves, follow-up observations suggested that subtle signs crept through that might signal to patients which groups they were in.  Modern medical tests often are conducted as double-blind tests, in which the researchers in direct contact with patients do not themselves know which sort of treatment they are giving out;  treatments are assigned randomly by researchers one level removed from the procedures and tracked by code numbers.  As far as the treating personnel are concerned, there is only the one group.
The effects of numbers.  In biology especially, there is enough variety among individual organisms or even laboratory systems that any single individual or set-up might be unusual and produce unusual results.  The more individuals that can be used and "blended" into a statistical response, the less probable that blind chance will have a significant effect on outcomes.  You know that people you know have traits that aren't exactly universal human traits, and it would not be scientific to use just one of them to make pronouncements about humanity as a whole.  Single- or limited-case evidence is called anecdotal evidence and is a driving force of the "health supplement and herbs" industry, where a couple of success stories are used to imply that everyone can benefit from their product.  Good scientific studies involve large numbers of test subjects or many repetitions, so odd rare results have little impact on the overall data.
  
 
 

SCIENTIFIC METHOD - PERFORMING EXPERIMENTS

  
 

 If all goes well, the actual doing of the experiment will be the easy part, with all the procedures and methods mapped out ahead of time.  Care must be taken to keep the control tests comparable to the experimental tests, or new variables may creep into to process.
All data must be recorded in an organized fashion according to the experimental design.  A good experimenter also keeps extensive notes as they go along, relying on their training and powers of observation to notice things beyond what is being officially measured.  The best researchers record everything that they notice, without trying to pick out what might be important.  It may turn out later that a seemingly minor happening has major significance - using our green chemical example, it could happen that a small notation about how the extraction process is working might later supply a big clue to just exactly what the green chemical is.
In biology especially, often things happen during the experiment that were not anticipated in the experimental design, and decisions have to be made about how to adapt.  Good scientists have to be good reactors and good decision-makers.
  
 
 

SCIENTIFIC METHOD AND EXPERIMENTAL RESULTS

  
 

 An experiment is run, measurements are made, data is collected, and the question posed by your original hypothesis should be answered.  It is always possible that the answer will be, "No, you were wrong, and here's the evidence."  Classically, results that don't support your hypothesis support the "you're wrong" null hypothesis.  Scientists, as human beings, don't much like hearing that they're wrong, and often won't take "no" for an answer.  The most difficult decision that a researcher makes is to resist the temptation to "rethink" an experiment and get it to support ideas it really didn't support.
The control become significant at this stage as a comparison - if both your experimental results and your control results are roughly the same, then the variable you were testing had no real effect.  If your headache medication results were an average of a 3.2 drop on your 10-point pain scale but your placebo group had an average of a 3.1 drop, your drug isn't very effective over just any old pill.
Statistics are an extremely important aspect of modern science.  This type of math can allow comparisons of things or groups that seem incomparable - an average or ratio can be used to compare groups of very different sizes, for instance.  There are very many ways to mathematically manipulate data, and statistics are famous both for its ability to find patterns that are not obvious and, unfortunately, for the flexibility they afford someone desperate to wring some sort of support out of their data.  This is why statistical analysis should be part of the starting design - if you plan ahead of time the best way to get useful information out of your results, you can resist the temptation later to just keep trying different statistical approaches until the results look like they are "supposed" to.
Conclusions, as mentioned earlier, are actually a new set of hypotheses built upon the results of the tests.  When you say, "This happened and this is what it means," others might think that your results indicate something entirely different.  As you become trained, you will find published studies where your own interpretation may be so contrary to the researchers' that it won't seem like they really looked at their own results.
  
 
 

MODERN SCIENCE AND THE NULL HYPOTHESIS

  
 

 According to classic Scientific Method, results that support the null hypothesis are just as valuable as those that support the working hypothesis;  however, in practice this is not really true.  Very rarely does a researcher write up and try to publish what doesn't work, and scientific journals tend to not be interested.
However, a researcher new to a field could certainly benefit by being able to see where the dead-ends are, and soon there may be a Journal devoted to negative results, at least in the biomedical field.
  
 
 

MATH AND MISINTERPRETATION OR MISREPRESENTATION

  
 

 How the numbers are presented from a study can be used to subtly affect how others will feel about them.  To say a certain factor triples cancer risk sounds more alarming than saying that the risk went from one-in-a-million to three-in-a-million, although neither statement is false or even really deceptive.  If you really need to understand a study, however, you need access to the actual data and an understanding of how the data was analyzed. 

Certain statistical approaches are reasonable only in some instances.  Averaging seems a perfectly rationale way to reduce extremes to a middle ground that can be better understood, but beware of the study group.  Remember, if you take a group of humans, men and women, with the idea that a "representative" human could be gotten by averaging the various traits they have, your "average human" would have one ovary and one testicle!  Some groups don't average well, for reasons that may not be as obvious as this example.
  
 
 

SCIENCE AND PUBLISHING

  
 

 Science is a type of community enterprise, with a deep and rich history and a huge network.  Although the internet and conferences play a part, much of what connects scientists with others in their fields are scientific journals.  A journal is different from a science magazine in the way that articles are published:  journal articles go through a process, called peer review, in which their submitted papers are critiqued by a group of other scientists in the same field.  There is a fair amount of give-and-take in the process, as the peers ask questions and request clarifications or make suggestions about the appropriateness of the paper for that particular journal.  At its best, peer review makes sure that research and the papers about it have high quality;  at its worst, it can stifle new and innovative ideas that established scientists may be resistant to purely because the ideas are new.
Peer review can also happen at other steps of the process, such as:  when funds are needed for research, it can be the first step;  in large laboratories, there may be peer oversight at many stages;  ideas are presented at conferences and receive input.  The concept of review by others in the field is important.
Virtually every field of study, and most subfields, has a journal devoted to that brand of research.  Many are available, at least partially, online.
  
 
 

SOME BASIC SCIENCE TERMS AND BIOLOGY USAGE

  
 

 This may be confusing, but an important aspect of biology is that its terminology is based upon a world that does not like to follow heavily-structured rules.
In other science courses, you may have been given lists of terms and definitions;  the terms will show up in biology sources, but the definitions may not be the same.  The term hypothesis, for instance, has a particular meaning in the Scientific Method as a working explanation that can be tested, very much a preliminary idea, and you may have been taught that a theory is like a hypothesis but widely-accepted as the probable explanation for some phenomenon.  You may also have learned that a law is a rule, an explanation or feature that always works a certain way, without exception.  NONE of those terms can be reliably tracked in biology - sometimes they fit, as in Cell Theory, sometimes they don't, as in the widely-accepted Gaia Hypothesis.  And there are virtually no laws in the classic sense in biology, because somewhere some living thing is breaking whatever rule someone has tried to stretch across the breadth of every organism on the planet.
This can be very disturbing to some students who like the feeling that the world can be reduced down to regular rules.  The best that you can do is get used to the idea that it doesn't work that way with biology.  Or shift to physics, although quantum physics seems as crazy as biology sometimes.  There are a couple of reasons why much of modern biology in performed on the molecular level, and one of those reasons is that molecules behave better than organisms.
  
 
  
  
 
 
  
  
 
  
 
  

Deductive reasoning applies general principles to predict specific results. Inductive reasoning uses specific observations to construct general principles. Here is a brief description of the steps in the hypothetico-deductive method:
Scientists make observations of processes and events found in nature.
The observations lead to questions: what is this, how does it work, why does it work the way it does? This may necessitate further observations to be made.
The questions are then asked in a form that suggests a possible explanation (hypothesis) for the observations. Scientists try to come up with all possible explanations and pit them against each others as alternative hypotheses.
Using the available knowledge and understanding of the related phenomena, the scientist makes a best guess at which of the alternative hypotheses is most likely to be correct.
Experiments are designed in such a way that one or more hypotheses are tested. This means that the experiment is geared specifically towards rejecting one's favored hypothesis: it is directly testing if that hypothesis is wrong. If the results are positive, the favored hypothesis is not rejected, but the alternative hypotheses may be rejected. If the results are negative, the favored hypothesis is rejected and one or more of the alternative hypotheses are accepted and further directly tested.
Often, two experiments are conducted at the same time. In one experiment, all the variables are kept constant except one, while the other experiment is called the control experiment, and in that experiment, that variable is left unaltered. The results of the two experiments are compared to each other using statistical methods to determine if the tested variable (the one not kept constant) indeed has an effect on the outcome.
After performing a series of experiments, a paper is written that provides some background information, describes the experimental methods and results, provides the statistical analysis, and draws conclusions from the results. The paper is then submitted for peer review and published in a scientific journal. We will take a look at some real scientific papers later on in the course, so you can see the structure and form of it and be able to find and read such primary literature.
Once all but one alternative hypothesis has been rejected over a series of experiments, the one remaining hypothesis is further tested. The hypothesis, if correct, can be used to make predictions which can be directly tested in subsequent experiments. Predictions provide a way to test the validity of a hypothesis.
As more and more studies are done and the hypothesis gets stronger and stronger (as all possible alternatives get rejected), it grows in its predictive power and it may also grow in its ability to explain a broader range of phenomena. Once a hypothesis reaches the stage at which it is supported with large amounts of evidence after repeated testing, it becomes a theory.
A theory is a body of interconnected concepts most strongly supported by scientific reasoning and experimental evidence. It is a scientific term that is used to denote the scientific concepts that have stood the test of time and are best supported by experimental evidence.
This sense of the word "theory" - the scientific ideas with the greatest certainty that they are correct - is in contrast to the colloquial use of the term, which means almost opposite - lack of certainty (as in "it's my theory that Secretariat was the greatest American athlete of all times", or "it's just a theory - nothing you should trust on its face"). Purveyors of pseudoscience (for financial, religious or political reasons) like to utilize the difference between the two senses of the word, dishonestly implying that a scientific theory they don't like is uncertain when just the opposite is true.
The strongest theories are those that are supported by a wide variety of kinds of evidence. Theory of evolution is one of the best supported theories of all science not only because it is backed up by mountains of evidence (and no evidence against it), but also because the evidence comes from many different areas of science: paleontology (fossils), biogeography, ecology, mathematical modeling, population and quantitative genetics, comparative genomics, medicine, agricultural breeding, study of animal behavior, comparative anatomy, comparative physiology and comparative embryology.
The way disparate data from quite different areas of science, when put together, all strengthen a single theory, is called consilience. Recently, this word has been misused in popular literature (including a book of the same name) and press to mean quite the opposite – taking the methodology or findings from one discipline and applying it to a variety of other disciplines, e.g., taking the logic of evolution by natural selection and applying it to chemistry, pharmacology, psychology or computer science. That is a worthy endeavor, but it not a correct meaning of the term ‘consilience’.
Sometimes you will see (as opposed to the image on p.5 of your textbook) scientific method schematically depicted like this:

There are two reasons why the Biology textbook does not show a graph like this: a) it is not applicable to biology, and b) it is wrong.
It is wrong because it places “law” above the theory. Actually, the opposite is true – many laws (in physics, for instance) are elements of a greater theory and are parts of the evidence that the theory is correct. Laws are usually mathematical depictions of regular behavior of some aspect of nature. In other words, laws describe nature but do not explain it. Theories explain nature and are thus on the top of the hierarchy of scientific knowledge.
The model above is inapplicable to biology (it was probably drawn by a physicist) because there are no laws in biology. There are rules (like Bergmann-Allen Rule in ecology or Cope’s Rule in evolutionary biology), there are generalizations (e.g., Scaling), there are mathematical models (e.g., in population genetics) and there are Principles (e.g., the Principle of Natural Selection), but there are no laws. Biology deals with processes at much higher levels than does physics, where emergent properties of complex systems introduce a dose of unpredictability. All potential “laws” in biology have many exceptions, or have to be limited to a very small subset of processes, or to a small subset of organisms – they are not exception-less as laws of physics are.
Hypothetico-deductive method described above, while arguably the most powerful part of the scientific method, is not the only one. There is a continuum of scientific “methods” as depicted here (from Brandon 1996):

Collecting the information about all the species of birds and salamanders in the mountains of North Carolina is not a test of hypothesis and is not manipulative (and is not experimental) – yet it is certainly science (place a dot in the bottom right corner of the graph) – it provides important information about the natural world. If patterns emerge from such a survey and prompt new ideas about species distribution, this can then be tested in a more experimental fashion.
Human Genome Project is highly manipulative (and expensive!), yet it is not hypothesis-testing (place a dot in the bottom left corner). Nobody predicted that we would find anything but the four nucleotides known to make up DNA. We had no predictions as what the sequence will be and what would it all mean. Once the work was done, we could use the HGP as a tool for testing new hypotheses, e.g., how many genes do we have, how they are related to the genes of chimps, how diverse are particular gene sequences in human population as a whole, etc.
Paleontology is somewhere in the middle. It is somewhat manipulative (it takes hard work and a lot of people to do it) and it is somewhat hypothesis-testing (place a dot smack in the middle of the graph). Paleontologists do not dig randomly – they dig in particular places on the planet in particular layers of the sediment, looking for fossils of particular kinds of organisms. For instance, a group recently did an excavation in a particular bed of Late Silurian layer, looking specifically for a fossil of an early tetrapod, i.e., a transitional organism between fully aquatic and fully terrestrial mode of life. They discovered exactly that – a fossil named Tiktaalik whose fins were better suited for walking on land than that of fishes (like mudskippers, catfish and lungsfish), yet not completely evolved for land use as in amphibians.
Sometimes nature provides an experiment that tests a hypothesis (a dot in the top right corner). For instance, a biogeographical model of island succession was tested when the volcano Krakatoa erupted and eliminated all life from the island. The scientists went there and observed which organisms flew in from the mainland, in which order, and how the ecosystem passed through several stages until it reached its mature stage, thus confirming (and somewhat modifying) their hypotheses.
No matter how strongly a theory is supported by empirical evidence, it is always theoretically conceivable that one day, some data will come in that will force the scientists to modify or even eliminate the theory. Even if the scientists are 99.999999999999999999999999999999999% certain that the theory is true, it is philosophically incorrect to say that it is 100% true and to call it the Truth with the capital T. That is why scientists, when interviewed in the media, often sound uncertain and wishy-washy, while some quack or pseudoscientist pronounces his absolute certainty. Audience not educated in the scientific method is likely to swallow the pseudoscience bait, hook and sinker because we, as humans, crave certainty. It takes some scientific training to be able to fully embrace and even love uncertainty. That is why it is difficult for scientific knowledge to counteract financially, religiously and politically motivated assaults on it. However, nature does not care about what we like and wish for: the apples will continue to fall down, the continents will continue to move around the globe (causing earthquakes and volcanic eruptions) and the organisms will continue to evolve whether we like it or not, whether we believe in it or not.