Introduction to Biology

Molecules and Cells

Chapter 2 - Science 

Scientific Method as an Accepted Approach

The study of the world and its workings have been going on for as long as people have exercised those bits of their primate minds that make them need to find patterns and explanations for things.  Humans are very good at taking in limited information and processing it to make predictions, and that allowed our ancestors to become excellent hunters and gatherers, even when moving into regions where the old rules didn't always apply.

For many centuries, science was mostly a loose aggregate of explanations that someone came up with and others were willing to accept:  thunder and lightning were the product of angry gods, for instance.  Ideas weren't tested in any organized way, so even explanations that were pretty poor could manage to last a long time.

Because of the way that humans communicate, especially with the development of writing, knowledge could accumulate, and methods could improve.  By the Renaissance, some naturalists were developing sets of rules by which explanations for things which happened could be examined and tested, and explanations could be supported with good test results or contradicted with bad ones.

Failed caveman experiment.
 

And another.
 

This one seemed like a better idea...
 

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.

One classic example of this is the series of tests done by Francesco Redi of the idea that rotting meat would turn into maggots.  This concept was based on an old idea, spontaneous generation, that allowed dead and waste materials to turn into new living things.  It doesn't work that way, but there were enough times that it seemed to work this way that folks accepted it.

Redi thought that something was getting to the meat to produce the maggots, so he set up a test with two comparative situations:  an open container with meat in it, and a closed one with meat in it.  No maggots appeared on the meat in the closed container.  However, some people thought that spontaneous generation would have worked, but was prevented by sealing air off from the closed container;  Redi needed to rerun his experiment with his "sealed" container just covered with a cloth.  The covered container still produced no maggots, supporting the idea that the meat by itself couldn't just turn into them.  Redi suspected and we now know that flies must lay eggs on the meat, which hatch into maggots that will eventually become flies, but that knowledge had to be gotten bit-by-bit over time, with experiments like Redi's as a start.

Redi and his connection to Galileo.
 

Spontaneous generation as an idea didn't go away quickly.
 

Image of Redi.
 

Maggot mysteries.
 

Other ideas that stayed around long after being shown to be false.

Over time, a system gradually developed by which ideas could be tested and reliable answers could be gotten.  The system had a few basic features:

It would only deal with testable ideas.  The whole system is a set of rules about testing ideas and predictions.  If ideas are not testable, they are outside of the scope of science.  This is why scientists are very resistant to including religious ideas in science education.  It isn't that they are anti-religion (some are, it's true), but they know that including untestable explanations for the world will contradict the basics of science that are being taught.

It would rely on logical thinking.  Logic itself has rules for analysis and moving to a conclusion which apply very well to science.  Although not everyone agrees on what is "logical," having this kind of structure can help people work through disagreements.  The problem is that much of science has to do with interpreting confusing bits of evidence to make conclusions, and logic can only get you so far sometimes.

You can't prove an idea, but you can disprove one.   You may have a very good explanation for why something happens, and time and again the evidence supports your idea, but you can never be completely sure that another explanation wouldn't work better.  However, if you're wrong, testing may quickly demonstrate that the world does not work the way you think it does.  This is called falsifiability.  The idea contrary to the tested idea is also called the null hypothesis.

Ideas are constantly being modified, even discarded, as new tests lead to new conclusions.  This is a critical part of advancement.  It may be why the big revolutions in science, where old ideas are replaced by new ones, tend to be done by young scientists:  older scientists become comfortable with their explanations, and a new generation is needed to bring a different perspective.

A page on testability.

An introduction to formal logic, which is very mathematical.

Some more logic rules.

On the null hypothesis.

The structure of scientific revolutions.

Some history on the Renaissance's Scientific Revolution.

Methods are subject to peer review.  Science is "steered" by people who understand the science.  These peers may be involved in analyzing hypotheses, or looking over an experiment's design, or checking results, to catch problems in the process.

Many researchers work in situations where no one around them may know as much about their particular subject as they do, and for them peer review may not happen until very late in the process.  The most common peer review happens when researchers send their reports to be published in scientific journals.  Editors of such journals have lists of reviewers for every specialty, and those reviewers will look over every aspect of the research.  They may reject the paper outright, or ask for clarification, or question the conclusions.  It is not a perfect system, but it helps to get things "right" before they are out there for other researchers to use as their own indirect observations.

More on peer review.

Peer review applied to students' work.

Scientific Method:  the Steps and Terms. 

The system developed over the last several centuries has some basic steps that a good scientist must acknowledge.  Sometimes an experiment follows the steps very closely, and sometimes the nature of what's being tested means that the rules can't be followed, exactly.

Observations are made of the workings of the universe.  This sound big and grand, but an observation can be something as simple as, "My tummy hurts."  Observations represent the taking in of information from the world.  Observations may be direct, taken in by a person's own senses;  you probably trust observations you personally have made with your own senses.  However, there are a couple of ways that observations can be indirect.  One type of indirect observation involves the use of technology.  Machines allow you to see things you can't really see (like with microscopes or telescopes), or hear things outside of a human hearing range, or even detect things are senses aren't built for (like magnetic fields).  We trust the machines to give us accurate information.  The other type of indirect observation involves using other people's observations, when they tell or write about them.  We can develop ideas based on what someone else has seen, or heard, or detecting with a machine.

Sometimes even our senses can be fooled:  optical illusions.
 

Illusion types, with links.
 

Artistic images taken with microscopes.
 

The unreliability of eyewitnesses.

From those observations, explanations are formed.  Such an explanation is a hypothesis.  If you see something happen an think you know why it happened, you have formed a hypothesis.  For science, though, a good hypothesis needs to have two critical features:

A good hypothesis should lead to good predictions.  "If this hypothesis is true, then this should happen..."   It isn't enough to ask, "What'll happen when we do this...?"  You need to produce predictions for the second critical feature -

A good hypothesis should be testable.  This is part of the basic concept of science:  it's all about the testing of ideas.  It may look like science, it might sound like science, but if it isn't testable, it isn't really science.  Often an idea is too big or complex to test, and must be split into testable bits.

The myths on Mythbusters are hypotheses;  the tests are sort of scientific.

Tests of hypotheses follow particular forms.  The "meat" of science is designing the tests for hypotheses.  A good test takes a lot of skill and imagination, and carrying them out often involves making adjustments as things veer away from the plan.  Tests may take the form of controlled experiments, which usually take place in laboratories, and field tests, which happen out in the world and are trickier to design.  It is common to use models as substitutes for subjects that can't really be tested in a lab:  mice may be used to see what a new drug's toxicity levels are, or computer simulations are used for weather and climate systems.

Tests should be focused.   A test should address a particular aspect of a question, and aspects within the question must be clearly defined.  "Is being friendly to a stranger likely to get them to help you?" is an interesting question, but to test it, you need some particular behavior that will be your "friendly" term, and a clear idea of what form of "help" you'll be looking for.  Even the "stranger" part of the test is open to definition:  how different from your strangers do you want your tester to be?  Sometimes two tests that seem to have conflicting results actually had very different definitions for what looked like the same factor.

Tests require a comparison test.  The test above needs a comparison, maybe two - will you get help from a stranger if you are not friendly, or are even unfriendly?  If you can, then the help doesn't seem related to your friendliness at all.  You could only know that with comparison tests.  The classic comparison in experiments is called a control, and the classic control test duplicates the experimental test, with the object being tested removed.  The object being tested is the experimental variable (or one of them, but the only one we'll be discussing here).  Many experiments can't follow this classic pattern, since removing the tested object by itself may not be possible, and many control tests just vary the variable, or check the impact of confounding factors (defined below).  In field tests, running real controls or even good comparisons can be impractical or impossible;  this makes the conclusions from such tests less reliable.

Tests should address recognizable confounding factors.  There are almost always aspects of a test that might affect your results but aren't what you're testing - those are confounding factors.  Many confounding factors are part of the experimental procedure, and their effects on results are called artifacts.  For instance, testing a new drug requires two test groups - both get "treated," but the controls don't get the drug in the pill or shot.  They must get the treatment, though, to control for the placebo effect:  just the act of treating people will improve the conditions of some members of the test group, enough to show up in the results.  If both groups get treated, it's assumed that the placebo effect is equal in the two groups.  As drug tests have developed over the last century, part of the control design involved a single blind, where the case patients and control patients were not told which group they were in.  This made sense, since knowing whether your treatment was "real" or not would affect placebo effect.  Then a researcher found that if the administering doctors know who is in which group, they can subtly give it away to the patients, and tests became double blind, where they don't know who is in which group (the treatments are randomly split up before the doctors get them).  All sorts of things can be confounding factors, and sometimes they aren't recognized until the tests are under way.  A common confounding factor is investigator bias:  researchers see what they expect to see.  A philosophical concept called postmodernism addresses how a person's own internal influences, from personality, upbringing, and culture, can strongly affect the way they see the world;  this can also affect how researchers form their hypotheses, design their experiments, and see their own results.  There are often ethical limitations on what may be done, which is a type of postmodern bias.

Tests should be reproducible by others.  If other people can't repeat your experiment and get similar results, then something odd is going on - you could be "steering" your results without being aware of it, or your particular test has an unrecognized confounding factor that changes for other testers.

A page on experimental design (uses some slightly different terms).

Mythbusters talking-to-plants design, with control but limited scope.  Plus, the test develops a big confounding factor.

Experimental design in psychology experiments.

Example of confounding factor effects.

How confounding factors can affect cancer research.

Control group definition, with example.

A study comparing placebos:  fake pills against fake acupuncture.

Why is the placebo effect stronger now than it used to be?

Using single and double-blind in a different context.

Getting reliable results is somewhat affected by chance - if you test a drug on one person and it really helps them, or the one person dies, how much do you know about the effects of your drug.  Could you even say that the drug caused the death?  Good tests require repetition.  A reliable drug test should use as many subjects as possible to reduce the impact of the "oddball" results.  Sometimes it's not a lot of subjects, but just doing the test over and over to see how often certain results occur.

Results are usually statistically analyzed.  Data is gathered - something is counted or measured through the course or at the end of the test, but how do you know what the numbers mean?  There are many ways to process the numbers, some of which are particularly used for certain types of tests.  It makes checking the conclusions difficult, especially if it isn't entirely clear just how the numbers have been crunched.  This is another way that two apparently similar tests can come to very different conclusions.  Statistics can also be used to distort results until they look like they support the hypothesis, and some times the researchers don't even know they've done it - they have just changed statistical methods until they have gotten a "good fit" with their data.

Because so much math is used in science, scientists prefer quantitative data, data in number form, to qualitative data, in a more subjective form.  If you were in a study of painkillers, it is likely that you would be asked to rate your pain on some sort of defined number scale - your pain is qualitative, but it would be converted into quantitative data.

Trying to figure out if results are reliable, reproducible.

Introduction to experimental statistics.

Results are what you make of them - forming conclusions.  Part of the method of scientific method is that you should actually use your results to get to a conclusion, and usually that is obviously true.  However, a conclusion is an interpretation of your results, and those can be very different depending upon who is doing the interpreting.  You might think that results would be pretty clear-cut, but often they are not.
 

A blog entry on questionable conclusions.

Go On to Next Chapter - Classification

Introduction to Biology - Molecules & Cells.
For SC-135.

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