Introduction to Scientific Method:using logical reality checks in science and other areas of lifeThis page is a beginning. It will help you understand the simplicity of scientific method in the simple "reality check" that is the main focus of scientific logic. But along the way you'll see other aspects of scientific thinking skills, and at the end you'll be invited to continue your exploration of “how it all fits together in a big picture” so you can understand the complexity of scientific methods and their potential applications in science education.
The Foundation of Scientific Method — logical Reality ChecksYou can understand and enjoy the adventure of science, because the thinking used in science is not strange and mysterious, it's the same thinking you use in daily life. In scientific logic, as in daily life, you use reality checks to decide whether "the way you think the world is" matches "the way the world really is." We'll begin by looking at the central activity of modern scientific method, when OBSERVATIONS (from an Experiment) and PREDICTIONS (based on a Theory) are compared in a REALITY CHECK that is a test of quality for a Theory:MAKING OBSERVATIONSIn science, information about nature comes from our observations. Consider two types of observation-situations:a) For several months you make observations about the moon's appearance, and the times and locations of its rising and setting. b) You observe the growth of young plants in many contexts, by varying many factors: light, temperature, type of seed (lima bean,...), type of soil, amount and frequency of watering, amount and type of fertilizer, treatment of seed (by soaking, cooking,...) before planting, and more. You try different combinations of factors, and for each experiment you make observations both above and below the soil surface, before and after the plant grows through the surface. A) In an uncontrolled observation-situation (like observing the moon) the situation is set up by nature. B) In a controlled observation-situation (in the designed experiments with seeds) humans set up the situation, but "what happens" depends on nature. The degree of human control, in setting up an observation-situation can range from no control through partial control to total control. Observations throughout the range-of-control can be logically compared with predictions to allow reality checks. Therefore, in this page all observation-situations will be called experiments, even though in general the term "experiment" is reserved for situations that are at least partially controlled. We observe using human senses (to see, hear, touch, taste, or smell) plus instruments (watch, ruler, scale, pipet, compass, thermometer, microscope, telescope, spectrometer, chromatograph,...) that help us measure more precisely and observe more widely. We translate raw data (from senses or instruments) into "observations" that we record using symbolic representations that are verbal (words,...), visual (pictures,...), and mathematical (numbers,...). I.O.U. — Later, this section will include more ideas about the creative generation and critical evaluation of "opportunities for observation" during the process of experimental design. THEORIES allow PREDICTIONSA theory is a human attempt to describe and/or explain our observations of what happens, or (in historical science) what has happened. A descriptive theory claims only to describe what happens. An explanatory theory claims to describe what happens and also why it happens.With a descriptive theory, predictions are made by generalizing, in an extrapolation that assumes "what happened before – in situations that are similar (but usually not identical) – will happen again." Or, you could view this prediction as a use of if-then deductive logic: "If this situation is similar to previous situations (as claimed by the theory), then we should expect a result that is similar." For example, you could predict the time and location of the moonrise on Wednesday, by extrapolating based on patterns you recognized while thinking about observations from Sunday, Monday, and Tuesday. An explanatory theory claims to explain "how and why things are happening" in an experimental system by describing — verbally, visually, and/or mathematically — the system's composition (what it is) and operation (what it does). You can use this model to make predictions by answering the question, "In this situation, if the composition-and-operation model is true (as claimed by the theory), then what will happen and what will we observe?" With either type of theory, the if-then inference is similar. You run a mental experiment by thinking, "In this situation, IF the system behaves as expected (according to the theory), THEN we will observe ___" and what you put in the blank is your theory-based prediction. When scientists make an if-then inference, they can move from IF to THEN in a variety of ways. They might remember what has happened before, and extrapolate from this past into the future by estimating how similarities and differences in situations (when comparing previous situations with the current situation) will translate into similarities and differences in observations. Or, if a theory includes an equation or a model, they could substitute numbers into the equation and calculate, or "run the model" in their minds or in a physical simulation or computer simulation. If predictions can be made using several methods, this will serve as a check on the predicting methods and a cross-check on the predictions. A theory-based inference about "what will happen and what will be observed" can be made either before or after observations are known. When an inference is made after observations are known, there is more concern about unconscious bias or conscious cheating, since non-valid logic could be used in an attempt to achieve a match between the inference and the known observations. But inferences with either timing are logically equivalent if each is obtained using valid logic, and in science both are called "predictions". But do scientists typically design theories? In their daily work, scientists
rarely design large-scale generalized mega-theories,
such as the theories of gravity, invariance, or evolution developed by Newton,
Einstein, or Darwin. Instead, usually they are applying generalized
theories that already are accepted, in their study of particular experimental
situations for which they are designing small-scale specialized sub-theories. For
example, a group of chemists might apply generalized theories (atomic theory,
quantum mechanics, kinetics, thermodynamics,...) to a particular experimental
system, or a collection of systems,
in an effort to design a sub-theory that seems
to
be
true (and/or
useful) for these systems. You can be a scientist, generating your own theory to explain moon phases, by "running a model" for the sun-earth-moon system.* Darken a room, turn on a lamp to be the sun, use one ball for the earth — with a marker (a sticker, a pin,...) to show your location on earth, so you can imagine "what you will see" from that location — and another ball for the moon. By looking at the effects of earth's rotation around its axis, and the moon's orbit around earth, you should also be able to explain why the sun sets in the west, and why there is a pattern for moon phases and the associated times of moonrises and moonsets.
Part 2
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1. Hypothetico-Deductive Logic, and Empirical Factors in Theory Evaluation 2. Conceptual Factors in Theory Evaluation 3. Cultural-Personal Factors in Theory Evaluation 4. Theory Evaluation (using critical thinking) 5. Theory Generation (using creative thinking) 6. Experimental Design (Generation-and-Evaluation) 7. Problem-Solving Projects 8. Thought Styles (cultural and personal) 9. Mental Operations (creative-and-critical thinking) |
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The diagram to the right, which is similar to the one above but with less detail, shows 9 aspects of Scientific Method: 3 of them [123] for evaluation factors (empirical, conceptual, cultural-personal), another 3 [456] for the designing (generation & evaluation) of theories and experiments, and 3 [789] for the process of science (problem-solving projects, thought styles, productive thinking).
Because my model shows the functional relationships between these 9 aspects of science by integrating their relationships
into a coherent framework, I call it Integrated Scientific
Method. This model of scientific thinking, for the exploratory process of inquiry used by scientists when they ask questions and try to find answers, is a unifying synthesis of ideas (mainly from scientists and philosophers, but also from sociologists, psychologists, historians, and myself) that can be useful for understanding the methods of problem solving used in science, and for helping students improve their skills with these methods of thinking.
My model of scientific methods is not THE Scientific Method, and its 9 parts are aspects of science rather than steps in a sequence. This model is outlined, verbally and visually, in A
Basic Overview of Scientific Method and is examined with more depth in A
Detailed Overview of Scientific Method which is a condensation of the first
half of my PhD dissertation; the second half was the application of this model for the integrative analysis of a science-inquiry classroom.
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In the page you're now reading, you already have seen some of the nine perspectives on scientific thinking-and-action. In this page the main focus has been "reality checks" using hypothetico-deductive logic (1), but you've also seen the process of theory generation (5) and theory evaluation (4) and how a theory is designed using "quality checks" with the criteria for quality based on a combination of empirical factors (1) and conceptual factors (2) and cultural-personal factors (3), and everything is infused with the productive mental operations (9) of creative idea-generation and critical idea-evaluation.
A Model for Design Process This page includes a brief outline [which needs revising - iou] of Science as Design. You can learn more about design strategies — which are used for almost everything you do in life — in An Introduction to Design which gives real-world examples of problem solving and the process of design, plus outlines of design-and-science relationships (which are explored more deeply in Design & Science) and educational applications of Design Process & Scientific Method; and An Overview of Design Process offers a "modes of thinking-and-action" perspective that complements An Introduction to Design. |
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Continued Explorations of Science, and Applications for Education
Learning a basic model of scientific method, as in
this page, is a good way to begin. But usually it isn't a good place
to end. If you want to understand science more completely, and appreciate
it more fully, I encourage you to continue your journey of exploration beginning with the Basic
Overview of Integrated Scientific Method (link is above) which will help
you learn more about the fascinating complexity of science. You can also
study the hot debates about science: Should
scientific method be eks-rated?
And in an occasionally controversial
area, educators (plus parents, school boards,...) think about the goals
and methods of general education and science education. In all types
of schools — public, private, and home, from K-12 through college — teachers
are wondering how the logical
thinking skills of science can be taught more effectively in the classroom. They want to offer
a quality education that includes scientific concepts plus thinking skills,
so students will be motivated to learn, and will learn how to think more often
and more effectively, with enthusiasm and skill. A wide variety of questions — about
creative-and-critical thinking, curriculum design, teaching strategies,... — are explored in the
areas for thinking skills & teaching methods. [update: In early 2012, I began making a "condensed website" for these ideas, Using Design Process in Problem Solving and Education, which I recommend; the next three links are for the older web-pages that I'm condensing.] A good starting place is the conclusion of An Introduction to Design which leads to Problem Solving & Metacognition in Education. Also check An Overview of Design Process.
And for continued exploration, you can see what's available in an overview-sitemap
for Design Process & Scientific Method
in Education — Developing a Curriculum for Thinking Skills and Problem Solving where you'll see links to pages asking "How can we use Aesop's Activities for goal-directed education?" and more.
APPENDIX
Semi-Explanatory Theories
Philosophers of science
ask, "What is required for an adequate explanation?", and
they don't offer easy answers. They say that even though distinguishing
between description and explanation can be useful, usually it isn't
entirely accurate because most modern theories claim to explain some
things but not everything.
Consider, for example, Newton's
theory of gravity. It describes what happens: two objects attract
each other with a force of GMm/R2 where G is a constant of
nature, M and m are the objects' masses, and R is the distance between
their centers. It also provides some explanation: gravitational
force is caused by interaction between the masses of any two objects,
anywhere in the universe. But the explanation is not satisfactory
at deeper levels, when we ask additional questions about how and why: How
is gravitational force produced? Is it associated with an elementary
particle, a graviton? Is it related to strong, weak, and electromagnetic
forces? How is it transmitted through almost-empty space between
the earth and moon? Why does gravity exist? For these questions,
scientists still don't have satisfactory answers.
And other semi-explanatory
theories — which can claim "some explanation but not
a full explanation" — occur in other areas of science.
Visual Representations of Thinking in Science Mode and Design Mode:
Below, the third diagram combines ideas from the top two diagrams
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This section has a dark-gray background and small font-size because it's not worth reading. Later, I'll either revise it or will remove it. Until then, you should just ignore it. Science as Design — Science Process in the context of Design Process Combining two thinking strategies in
Scientific Method: The basic process of design, shown in the diagram below, begins when you choose an Overall Objective that is the thing (the product, strategy, or theory) you want to design. Imagine that you want to design a product. After you define Goals (for the properties you want the product to have), you generate options (by searching for old products, or inventing new products) and for each option you evaluate its quality (as defined by your goals) in a Quality Check, either by comparing goals (for the desired properties of a product) with predictions (about the actual properties of this product-option) or by comparing goals (for desired properties) with observations (of the actual properties for this product-option). You continue this process of generating options and evaluating them (by using one or both types of Quality Checks) until you decide that one of the options is satisfactory, or you abandon the search. / This is a simplified quick-and-rough sketch of the design process, which is described in more detail (and illustrated by the design of a hybrid minivan in the 1980s) in An Introduction to Design. image ( z-briefd.gif ) removed comment to reader: The rest of this "science and design" section (especially the ending) is rough-and-incomplete. It will be revised soon, probably in December 2011. For a treatment that is much more polished-and-complete, check the "science and design" section of An Introduction to Design. And I the diagrams of design at the end of this page will probably be moved (to another part of the page) or just removed. ==[[ also, many scientists like to see their goal as question-answering rather than problem-solving (with its common meaning) so later I'll explain why science is problem solving if this is defined, as in An Introduction to Design, as an opportunity for making things better, for moving from a now-state to a goal-state (where there is a gap in your knowledge) in which your knowledge has improved, with the current state viewed as a problem; ]] Science as Design: When scientists design a theory, they creatively
generate the theory, and critically
evaluate its quality by comparing its properties with the goal-properties they want a theory to have. For most scientists in most situations, the most important property of a theory is its empirical quality (which is evaluated observation-based Reality
Checks) but scientists also consider conceptual quality and cultural-personal quality when evaluating a theory. Their GOALS for a theory — which are the desired properties of
a theory — include three types of factors: empirical,
conceptual, and cultural-personal. The main focus of scientific method is reality checks, while quality checks are the main thinking tool in the process of design-thinking that we use for doing almost everything in life, when we design theories (in science and in other areas, including everyday life) and products (things we make and use) and strategies (for a wide variety of strategy-decisions in many areas of life). such as the technological design-applications of science, and the use of scientific reality checks by a designer who is searching for truth so the products and strategies being designed will have a solid foundation in "the way the world really is" / where design is the process of combining creative generation with critical evaluation / { The process of design is similar for a theory, as outlined below, or for an experiment, as described above. } |
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related pages are a sitemap for Aesop's Activities
for Goal-Directed Education: The area of THINKING
SKILLS offers |
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http://www.asa3.org/ASA/education/think/scientific-method.htm
Copyright © 2004 by Craig Rusbult, all rights reserved.