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A good primer for cutting-edge energy technology researchers, who sometimes let good technique go, due to their disdain for academia's spurning of what could actually be of merit.
"We have to learn again that science without contact with experiments is an enterprise which is likely to go completely astray into imaginary conjecture."

— Hannes Alfven

Contents

An experiment, in the scientific method, is a set of actions and observations which is performed to verify or falsify a hypothesis or identify a causal relationship between phenomena. The experiment is a cornerstone in empirical approach to knowledge. An experiment (Latin, ex-+-periri, experimentum "of [or from] trying"; French, experiri "to try") is a trial or special observation. An experiment is undertaken under controlled conditions to disprove or confirm a hypothesis or to discover some unknown principle effect.

Experiments conducted in accord with the scientific method have several features in common. The design of experiments attempts to balance the requirements and limitations of the field of science in which one works so that the experiment can provide the best conclusion about the hypothesis being tested. In some sciences, such as physics and chemistry, it is relatively easy to meet the requirements that all measurements be made objectively, and that all conditions can be kept controlled across experimental trials. On the other hand, in other cases such as biology, and medicine, it is often hard to ensure that the conditions of an experiment are performed consistently; and in the social sciences, it may even be difficult to determine a method for measuring the outcomes of an experiment in an objective manner.


Steps for experiments

Observe
Collect evidence and make measurements relating to the phenomenon you intend to study.

Observation is an activity of an sapient or sentient living being, which senses and assimiliate the knowledge of a phenomenon in its framework of previous knowledge and ideas. Due to irreliability and irreproducebility of human senses, science does not use them, using instead various devices (spectrometers, oscilloscopes, cameras, etc) and tools (clocks, scales, thermometers, interferometers, rangers, etc). This dramatically increeases accuracy, quality and value of the information obtained. To abstract further from subjectivity of human mind, logic is used to explain observed facts and mathematics (which is highest form of logic) is used to model reality in order to accuratelyly predict its behavior in specific important circumstances (usually models have form of differential equations solved by computers). The accuracy and tremendous success of science is primarily attributed to the accuracy and objectivity of observation of reality science use. Reliance is placed upon the five physical senses: visual perception, hearing (sense), taste, feeling, and olfaction, and upon measurement techniques. It is therefore understood that there are always certain limitations in making observations.

Hypothesize
Invent a hypothesis explaining the phenomenon that you have observed.

A hypothesis is a suggested explanation of a phenomenon or reasoned proposal suggesting a possible correlation between multiple phenomena. The term is derived from the ancient Greek, hypotithenai meaning "to put under" or "to suppose." A scientific hypothesis must be testable and generally will be based upon previous observations or extensions of scientific theories. In early usage, scholars often referred to a clever idea or to a convenient mathematical approach that simplified cumbersome calculations as a hypothesis; when used this way, the word did not necessarily have any specific meaning. Cardinal Bellarmine gave a famous example of the older sense of the word in the warning issued to Galileo in the early 17th century: that he must not treat the motion of the Earth as a reality, but merely as a hypothesis.

In common usage at present, a hypothesis refers to a provisional idea whose merit needs evaluation. To be evaluated, the specifics of the hypothesis need to be defined in operational terms. A hypothesis requires more work by the researcher in order to either confirm or disprove it. In due course, a confirmed hypothesis may become part of a theory or occasionally may grow to become a theory itself. Sometimes a scientific hypotheses have the form of a mathematical model, thouygh a mathematical model is not necessary. Sometimes they can also be formulated as existential statements, stating that some particular instance of the phenomenon being studied has some characteristic and causal explanations, which have the general form of universal statements, stating that every instance of the phenomenon has a particular characteristic.

Any useful hypothesis will enable predictions, by reasoning including deductive reasoning. It might predict the outcome of an experiment in a laboratory setting or the observation of a phenomenon in nature. The prediction can also be statistical and only talk about probabilities. Karl Popper, following others, has argued that a hypothesis must be falsifiable, and that a proposition or theory cannot be called scientific if it does not admit the possibility of being shown false. By this additional criterion, it must at least in principle be possible to make an observation that would show the proposition to be false, even if that observation had not actually been made. A falsifiable hypothesis can greatly simplify the process of testing to determine whether the hypothesis has instances in which it is false. It is essential that the outcome be currently unknown or reasonably under continuing investigation. Only in this case does the experiment, test or study potentially increase the probability that the hypothesis be true. If the outcome is already known, it is called a consequence and should have already been considered while formulating the hypothesis. If the predictions are not accessible by observation or experience, the hypothesis is not yet useful, and must wait for others who might come afterward to make possible the needed observations. For example, a new technology or theory might make the necessary experiments feasible.

Propositions may come in the form of an assertion of a correlation between, or among, two or more things, but without asserting that there is necessarily a cause and effect relationship. If a particular independent variable is changed there also a change in a certain dependent variable. This is also known as an "If and Then" statement, whether or not it asserts a direct cause-and-effect relationship. A hypothesis about possible correlation does not stipulate the cause and effect per se, only stating that 'A is related to B'. Causal relationships can be more difficult to verify than correlations, because quite commonly intervening variables are also involved which may give rise to the appearance of a possibly direct cause and effect relationship, but which upon further investigation turn out to be more directly caused by some other factor than what is stated in the proposition. Also, a mere observation of a change in one variable, when correlated with a change in another variable, can actually mistake the effect for the cause, and vice-versa (i.e., potentially get the hypothesized cause and effect backwards).

Empirical hypotheses that have been repeatedly verified may become sufficiently dependable that, at some point in time, they become considered to be "proven" and are then termed laws. Alternately, such repeatedly verified hypotheses may instead be referred to simply as "adequately verified," or "dependable." The hypothetico-deductive method demands falsifiable hypotheses, framed in such a manner that the scientific community can prove them false (usually by observation). (Note that, if confirmed, the hypothesis is not necessarily proven, but remains provisional.) Researchers weighing up alternative hypotheses may take into consideration: testibility (compare falsifiability), simplicity (as in the application of "Occam's Razor", discouraging the postulation of excessive numbers of entities), scope (the apparent application of the hypothesis to multiple cases of phenomena), fruitfulness (the prospect that a hypothesis may explain further phenomena in the future), and conservatism (the degree of "fit" with existing recognised knowledge-systems).

Predict
Use the hypothesis to predict the results of new observations or measurements. Often, advanced mathematical and statistical hypothesis testing techniques are used to design experiments that attempt to effectively test the plausibility of hypotheses.

A prediction or forecast is a statement or claim that a particular event will occur in the future. The etymology of this word is Latin (from præ- "before" plus dicere "to say"). Outside the rigorous context of science, prediction is often confused with informed guess or opinion. A prediction of this kind might be valid and useful if the predictor is a knowledgeable person in the field and is employing sound reasoning and accurate data. Large corporations invest heavily in this kind of activity to help focus attention on possible events, risks and business opportunities, using futurists. Such work brings together all available past and current data, as a basis on which to develop reasonable expectations about the future.

Predictions have often been made, in pre-scientific times and still today, by resorting to paranormal or supernatural means, such as prophecy. In a scientific context, a prediction is a rigorous (often quantitative) statement forecasting what will happen under specific conditions, typically expressed in the form If A is true, then B will also be true. The scientific method is built on testing assertions which are logical consequences of scientific theories. This is done through repeatable experiments or observational studies. A scientific theory whose assertions are not in accordance with observations and evidence will probably be rejected. For example, theories such as String theory make no testable predictions, and thus remain protosciences until testable predictions are known to the community. Additionally, if new theories generate many new predictions, they are often highly valued, for they can be quickly and easily confirmed or falsified (see predictive power). In many scientific fields, desirable theories are those which predict a large number of events from relatively few underlying principles. Quantum physics is an unusal field of science because it enables scientists to make predictions on the basis of probability.

Mathematical models and computer models are frequently used to both describe the behaviour of something, and predict its future behaviour. In microprocessors, branch prediction permits to avoid pipeline emptying at microcode branchings. Engineering is a field that involves predicting failure and avoiding it through component or system redundancy. Some fields of science are notorious for the difficulty of accurate prediction and forecasting, such as software reliability, natural disasters, pandemics, demography, population dynamics and meteorology.

Verify
Perform experiments to test those predictions. Attempting to experimentally falsify hypotheses is thought by many to be a better choice of term here.

Formal verification is the act of proving or disproving the correctness of a system with respect to a certain formal specification or property, using formal methods. Verification is one aspect of testing a product's fitness for purpose. Validation is the complementary aspect. Often one refers to the overall checking process as "V & V" (validation and verification). The verification process consists of static and dynamic parts. One can inspect (static) and run against specific cases (dynamic). Validation usually can only be done dynamically.

Evaluate
If the experiments contradict your hypothesis, reject it and form another. If the results are compatible with predictions, make more predictions and test it further.

Evaluation is the systematic determination of merit, worth, and significance of something or someone. Evaluation often is used to characterize and appraise subjects of interest. In the field of evaluation, there is some degree of disagreement in the distinctions often made between the terms 'evaluation' and 'assessment.' Some practitioners would consider these terms to be interchangeable, while others contend that evaluation is broader than assessment and involves making judgments about the merit or worth of something (an evaluand) or someone (an evaluee). When such a distinction is made, 'assessment' is said to primarily involve characterizations – objective descriptions, while 'evaluation' is said to involve characterizations and appraisals – determinations of merit and/or worth. Merit involves judgments about generalized value. Worth involves judgments about instrumental value. For example, a history and a mathematics teacher may have equal merit in terms of mastery of their respective disciplines, but the math teacher may have greater worth because of the higher demand and lower supply of qualified mathematics teachers. A further degree of complexity is introduced to this argument when working in different languages, where the terms 'evaluation' and 'assessment' may be variously translated, with terms being used that convey differing connotations related to conducting characterizations and appraisals.

Publish
Tell other people of your ideas and results, and encourage them to verify the claims themselves, in particular by inviting them to challenge your reasoning and check that your experimental results can be repeated. This can be under peer review, but can be done through any scholarly communications about the results (as many of the major breakthroughs in the history of science were naturally published without having undergone peer review).

Publishing is the industry concerned with the production of literature or information - the activity of making information available for public view. Traditionally, the term refers to the distribution of printed works such as books and newspapers. With the advent of digital information systems and the Internet, the scope of publishing has expanded to include websites, blogs, and other forms of new media. Publication is also important as a legal concept; (1) as the process of giving formal notice to the world of a significant intention, and; (2) as the essential precondition of being able to claim being first.

The development of the printing press represented a revolution for communicating the latest hypotheses and research results to the academic community and supplemented what a scholar could do personally. To understand the scale of the problem: about two centuries ago, the number of scientific papers published annually was doubling approximately every fifteen years. Today, the number of published papers doubles about every ten years. Modern academics can now run electronic journals and distribute academic materials without the need for publishers. Not surprisingly, publishers perceive this emancipation as a serious threat to their business model. In reality, the interests of scholars and publishers have long been in conflict. The purpose of copyright is to protect the capital invested in the "work" by the publisher and the wish of the scholar is to have the work as widely distributed as possible.

Today, publishing academic journals and textbooks is a large part of an international industry. The shares of the major publishing companies are listed on national stock exchanges and management policies must satisfy the dividend expectations of international shareholders. Critics claim that these standardised accounting and profit-oriented policies have come to the fore and now constrain more altruistic leanings. An alternative to the corporate model is open access, the online distribution of individual articles and academic journals without charge to readers and libraries. In spite of the fact that middle and lower level researchers now need to keep a day job, they have managed to find smaller alternatives to the mass market in the form of small press such as self-publishing, print on demand, and through the eBook format as well. Even though there is little market exposure in addition to the royalty cheques that are few and far between, these publishing alternatives provide an avenue that expresses diversity in styles and political views that the mass markets haven't seen in the last 10-15 years.

"Hard" vs. "Soft" science design

"Today's scientists have substituted mathematics for experiments, and they wander off through equation after equation, and eventually build a structure which has no relation to reality."

— Nikola Tesla

"Hard sciences", in contrast to "soft sciences", attempt to capture the idea that objective measurements are often far easier in the former, and far more difficult in the latter. In addition, in the soft sciences, the requirement for a "controlled situation" may actually work against the utility of the hypothesis in a more general situation. When the desire is to test a hypothesis that works "in general", an experiment may have a great deal of internal validity, in the sense that it is valid in a highly controlled situation, while at the same time lack external validity when the results of the experiment are applied to a real world situation. One of the reasons why this may happen is because of the Hawthorne effect. As a result of these considerations, experimental design in the "hard" sciences tends to focus on the elimination of extraneous effects, while experimental design in the "soft" sciences focuses more on the problems of external validity, often through the use of statistical methods. Occasionally events occur naturally from which scientific evidence can be drawn, which is the basis for natural experiments. In such cases the problem of the scientist is to evaluate the natural "design".

Experiments conducted in accord with the scientific method have several features in common. The design of experiments attempts to balance the requirements and limitations of the field of science in which one works so that the experiment can provide the best conclusion about the hypothesis being tested. In some sciences, such as physics and chemistry, it is relatively easy to meet the requirements that all measurements be made objectively, and that all conditions can be kept controlled across experimental trials. On the other hand, in other cases such as biology, and medicine, it is often hard to ensure that the conditions of an experiment are performed consistently; and in the social sciences, it may even be difficult to determine a method for measuring the outcomes of an experiment in an objective manner.

For this reason, sciences such as physics are often referred to as "hard sciences", while others such as sociology are referred to as "soft sciences"; in an attempt to capture the idea that objective measurements are often far easier in the former, and far more difficult in the latter. In addition, in the soft sciences, the requirement for a "controlled situation" may actually work against the utility of the hypothesis in a more general situation. When the desire is to test a hypothesis that works "in general", an experiment may have a great deal of internal validity, in the sense that it is valid in a highly controlled situation, while at the same time lack external validity when the results of the experiment are applied to a real world situation. One of the reasons why this may happen is because of the Hawthorne effect.

Experimental design in the "hard" sciences tends to focus on the elimination of extraneous effects, while experimental design in the "soft" sciences focuses more on the problems of external validity, often through the use of statistical methods. Occasionally events occur naturally from which scientific evidence can be drawn, which is the basis for natural experiments. In such cases the problem of the scientist is to evaluate the natural "design". The first statistician to consider a methodology for the design of experiments was Sir Ronald A. Fisher. He described how to test the hypothesis that a certain lady could distinguish by flavor alone whether the milk or the tea was first placed in the cup. While this sounds like a frivolous application, it allowed him to illustrate the most important ideas of experimental design:

  • Randomization
  • Replication
  • Blocking
  • Orthogonality
  • use of factorial experiments instead of the one-factor-at-a-time method

Analysis of the design of experiments was built on the foundation of the analysis of variance, a collection of models in which the observed variance is partitioned into components due to different factors which are estimated and/or tested. In 1950, Gertrude Mary Cox and William Cochran published the book Experimental Design which became the major reference work on the design of experiments for statisticians for years afterwards. Developments of the theory of linear models have encompassed and surpassed the cases that concerned early writers. Today, the theory rests on advanced topics in abstract algebra and combinatorics. As with all other branches of statistics, there is both classical and Bayesian experimental design.

Planning research

Sampling

Variable

Independent

Dependent

Randomized controlled trial

Statistics and statistical theory

Controlled experiments

Many hypotheses in sciences such as physics can establish causality by noting that, until some phenomenon occurs, nothing happens; then when the phenomenon occurs, a second phenomenon is observed. But often in science, this situation is difficult to obtain. To demonstrate a cause and effect hypothesis, an experiment must often show that a phenomenon occurs after a certain treatment is given to a subject, and that the phenomenon does not occur in the absence of the treatment. Experimental controls are used in scientific experiments to prevent factors other than those being studied from affecting the outcome. Controls are needed to eliminate alternate explanations of experimental results. A researcher can use an experimental control, separating the rats into two groups: one group that receives the sweetener and one that doesn't. Two groups are kept in otherwise identical conditions, and both groups are observed in the same ways. Now, any difference between the two groups can be ascribed to the phenomenon -- and no other factor -- with much greater confidence. In other cases, an experimental control is used to prevent the effects of one variable from being drowned out by the known, greater effects of other variables.

A controlled experiment generally compares the results obtained from an experimental sample against a control sample, which is practically identical to the experimental sample except for the one aspect whose effect is being tested. The results from replicate samples can often be averaged, or if one of the replicates is obviously inconsistent with the results from the other samples, it can be discarded as being the result of an experimental error (some step of the test procedure may have been mistakenly omitted for that sample). Most often, tests are done in duplicate or triplicate. A positive control is a procedure that is very similar to the actual experimental test but which is known from previous experience to give a positive result. A negative control is known to give a negative result. The positive control confirms that the basic conditions of the experiment were able to produce a positive result, even if none of the actual experimental samples produce a positive result. The negative control demonstrates the base-line result obtained when a test does not produce a measurable positive result; often the value of the negative control is treated as a "background" value to be subtracted from the test sample results. Sometimes the positive control takes the form of a standard curve.

Controlled experiments can be performed when it is difficult to exactly control all the conditions in an experiment. In this case, the experiment begins by creating two or more sample groups that are probabilistically equivalent, which means that measurements of traits should be similar among the groups and that the groups should respond in the same manner if given the same treatment. This equivalency is determined by statistical methods that take into account the amount of variation between individuals and the number of individuals in each group. In fields such as microbiology and chemistry, where there is very little variation between individuals and the group size is easily in the millions, these statistical methods are often bypassed and simply splitting a solution into equal parts is assumed to produce identical sample groups.

Once equivalent groups have been formed, the experimenter tries to treat them identically except for the one variable that he or she wishes to isolate. Human experimentation requires special safeguards against outside variables such as the placebo effect. Such experiments are generally double blind, meaning that neither the volunteer nor the researcher knows which individuals are in the control group or the experimental group until after all of the data has been collected. This ensures that any effects on the volunteer are due to the treatment itself and are not a response to the knowledge that he is being treated. In human experiments, a subject (person) may be given a stimulus to which he or she should respond. The goal of the experiment is to measure the response to a given stimulus.

Field experiments

Field experiments are so named in order to draw a contrast with laboratory experiments. Often used in the social sciences, and especially in economic analyses of education and health interventions, field experiments have the advantage that outcomes are observed in a natural setting rather than in a contrived laboratory environment. However, like natural experiments, field experiments suffer from the possibility of contamination: experimental conditions can be controlled with more precision and certainty in the lab.

A field experiment applies the scientific method to experimentally examine an intervention in the real world (or as many experimental economists like to say, naturally-occurring environments) rather than in the laboratory. Field experiments generally randomize subjects (or other sampling units) into treatment and control groups and compare outcomes between these groups. Clinical trials of pharmaceuticals are one example of field experiments. Economists have used field experiments to analyze discrimination, health care programs, and education programs. Social psychologists generally avoid field experiments because of the context dependence of experimental outcomes. The latter statement is more true in modern times, but early in the history of social psychology, pioneers like Philip Zimbardo, Kurt Lewin and Stanley Milgram did many field experiments (Milgram did the famous "six degrees of separation" study mailing letters across the country to see how closely linked networks of strangers were).

Recent work by Glenn W. Harrison (University of Central Florida) and John A. List (University of Chicago) has established a taxonomy of field experiments. See their paper in the December 2004 issue of the Journal of Economic Literature for a complete treatment or List's website (http://www.arec.umd.edu/fieldexperiments/) for a quicker overview. Their taxonomy usefully partitions field experiments into three categories ranging from those that most closely resemble traditional laboratory experiments to those that are truly "Natural" field experiments in the sense that the subjects involved are unaware of any treatment taking place. (Note that artificial experiments in social psychology often use deception, so that subjects are also unaware of the true treatment).

Natural experiments

A natural experiment is a naturally occurring instance of observable phenomena which approach or duplicate a scientific experiment. Sometimes controlled experiments are prohibitively difficult or impossible, so researchers resort to natural experiments. Natural experiments rely solely on observations of the variables of the system under study, rather than manipulative control of those variables, as occurs in a controlled experiment. To the degree possible, they attempt to collect data for the system in such a way that the effects of variation in certain variables can be held approximately constant so that the effects of other variables can be discerned. The degree to which this is possible depends on the observed correlation between explanatory variables in the observed data. When these variables are not well correlated, natural experiments can be essentially as powerful as controlled experiments. Usually, however, there is some correlation between these variables, which weakens the power of natural experiments relative to what could be concluded if a controlled experiment were performed. Much research in several important science disciplines, including geology, paleontology, ecology, meteorology, and astronomy, relies on experiments of this type.

Observational studies

Observational studies are very much like controlled experiments except that they lack probabilistic equivalency between groups. These types of experiments often arise in the area of medicine where, for ethical reasons, it is not possible to create a truly controlled group. For example, one would not want to deny all forms of treatment for a life-threatening disease from one group of patients to evaluate the effectiveness of another treatment on a different group of patients. The results of observational studies are considered much less convincing than those of designed experiments, as they are much more prone to selection bias. Researchers attempt to compensate for this with complicated statistical methods such as propensity score matching methods.

Thought experiment

A thought experiment (from the German term Gedankenexperiment, coined by Hans Christian Ørsted) in the broadest sense is the use of an imagined scenario to help us understand the way things really are. The understanding comes through reflection on the situation. Thought experiment methodology is a priori, rather than empirical, in that it does not proceed by observation or experiment. Thought experiments are well-structured hypothetical questions that employ "What if?" reasoning. Thought experiments have been used in philosophy, physics, and other fields. They have been used to pose questions in philosophy at least since Greek antiquity, some pre-dating Socrates. In physics and other sciences many famous thought experiments date from the 19th and especially the 20th Century, but examples can be found at least as early as Galileo.

External articles and references

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See also

- PowerPedia main index
- PESWiki home page

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