Scientific Method

So we here on riu have one of the best research teams in the world "ourselfs" but alot of the experments I see on deffent threads do not follow the scientfic method ...people will change two or more parts of the experment they will not start with a hypothesis IE if i change this than it will resulte in this and they lack a contoll ...All experments should be able to be repeated by other members with the exact same resulte ive been meaning to make this thread for a while on the next few pages we shall go into depth on what the scientic method is and how it can better you plants and your yeilds .....

• The scientific method is a way to ask and answer scientific questions by making observations and doing experiments.
• The steps of the scientific method are to:
  • Ask a Question
  • Do Background Research
  • Construct a Hypothesis
  • Test Your Hypothesis by Doing an Experiment
  • Analyze Your Data and Draw a Conclusion
  • Communicate Your Results
• It is important for your experiment to be a fair test. A "fair test" occurs when you change only one factor (variable) and keep all other conditions the same.
• While scientists study how nature works, engineers create new things, such as products, websites, environments, and experiences.
  • If your project involves creating or inventing something new, your project might better fit the steps of the Engineering Design Process.
  • If you are not sure if your project is a scientific or engineering project, you should read Comparing the Engineering Design Process and the Scientific Method.

Overview of the Scientific Method

The scientific method is a process for experimentation that is used to explore observations and answer questions. Scientists use the scientific method to search for cause and effect relationships in nature. In other words, they design an experiment so that changes to one item cause something else to vary in a predictable way.
Just as it does for a professional scientist, the scientific method will help you to focus your science fair project question, construct a hypothesis, design, execute, and evaluate your experiment.


Steps of the Scientific MethodDetailed Help for Each StepAsk a Question: The scientific method starts when you ask a question about something that you observe: How, What, When, Who, Which, Why, or Where?
And, in order for the scientific method to answer the question it must be about something that you can measure, preferably with a number.
Your Question
Do Background Research: Rather than starting from scratch in putting together a plan for answering your question, you want to be a savvy scientist using library and Internet research to help you find the best way to do things and insure that you don't repeat mistakes from the past.Background Research Plan
Finding Information
Bibliography
Research Paper
Construct a Hypothesis: A hypothesis is an educated guess about how things work:
"If _____[I do this] _____, then _____[this]_____ will happen." You must state your hypothesis in a way that you can easily measure, and of course, your hypothesis should be constructed in a way to help you answer your original question.
Variables
Variables for Beginners
Hypothesis
Test Your Hypothesis by Doing an Experiment: Your experiment tests whether your hypothesis is true or false. It is important for your experiment to be a fair test. You conduct a fair test by making sure that you change only one factor at a time while keeping all other conditions the same. You should also repeat your experiments several times to make sure that the first results weren't just an accident.
Experimental Procedure
Materials List
Conducting an Experiment
Analyze Your Data and Draw a Conclusion: Once your experiment is complete, you collect your measurements and analyze them to see if your hypothesis is true or false. Scientists often find that their hypothesis was false, and in such cases they will construct a new hypothesis starting the entire process of the scientific method over again. Even if they find that their hypothesis was true, they may want to test it again in a new way.
Data Analysis & Graphs
Conclusions
Communicate Your Results: To complete your science fair project you will communicate your results to others in a final report and/or a display board. Professional scientists do almost exactly the same thing by publishing their final report in a scientific journal or by presenting their results on a poster at a

In its ancient usage, hypothesis also refers to a summary of the plot of a classical drama.
In Plato's Meno (86e–87b), Socrates dissects virtue with a method used by mathematicians,[2] that of "investigating from a hypothesis."[3] In this sense, 'hypothesis' refers to a clever idea or to a convenient mathematical approach that simplifies cumbersome calculations.[4] Cardinal Bellarmine gave a famous example of this usage 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.[5]
In common usage in the 21st century, a hypothesis refers to a provisional idea whose merit requires evaluation. For proper evaluation, the framer of a hypothesis needs to define specifics 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. Normally, scientific hypotheses have the form of a mathematical model. Sometimes, but not always, one can also formulate them as existential statements, stating that some particular instance of the phenomenon under examination 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 may also invoke statistics and only talk about probabilities. Karl Popper, following others, has argued that a hypothesis must be falsifiable, and that one cannot regard a proposition or theory as scientific if it does not admit the possibility of being shown false. Other philosophers of science have rejected the criterion of falsifiability or supplemented it with other criteria, such as verifiability (e.g., verificationism) or coherence (e.g., confirmation holism). The scientific method involves experimentation on the basis of hypotheses to answer questions and explore observations.
In framing a hypothesis, the investigator must not currently know the outcome of a test or that it remains reasonably under continuing investigation. Only in such cases does the experiment, test or study potentially increase the probability of showing the truth of a hypothesis. If the researcher already knows the outcome, it counts as a "consequence" — and the researcher should have already considered this while formulating the hypothesis. If one cannot assess the predictions by observation or by experience, the hypothesis classes as 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.
Scientific hypothesis

People refer to a trial solution to a problem as a hypothesis, often called an "educated guess"[6][7] because it provides a suggested solution based on the evidence. Experimenters may test and reject several hypotheses before solving the problem.
According to Schick and Vaughn,[8] researchers weighing up alternative hypotheses may take into consideration:

• Testability (compare falsifiability as discussed above)
• 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
• Conservatism – the degree of "fit" with existing recognized knowledge-systems.

Working hypothesis

Main article: Working hypothesis
A 'working hypothesis' is a hypothesis that is provisionally accepted as a basis for further research[9] in the hope that a tenable theory will be produced, even if the hypothesis ultimately fails.[10] Like all hypotheses, a working hypothesis is constructed as a statement of expectations, which can be linked to the exploratory research purpose in empirical investigation and are often used as a conceptual framework in qualitative research.[11][12]
In recent years, philosophers of science have tried to integrate the various approaches to evaluating hypotheses, and the scientific method in general, to form a more complete system that integrates the individual concerns of each approach. Notably, Imre Lakatos and Paul Feyerabend, respectively a colleague and a student, have produced novel attempts at such a synthesis.
Hypotheses, concepts and measurement

Concepts, as abstract units of meaning, play a key role in the development and testing of hypotheses. Concepts are the basic components of hypotheses. Most formal hypotheses connect concepts by specifying the expected relationships between concepts. For example, a simple relational hypothesis such as "education increases income" specifies a positive relationship between the concepts "education" and "income." This abstract or conceptual hypothesis cannot be tested. First, it must be operationalized or situated in the real world by rules of interpretation. Consider again the simple hypothesis "Education increases Income." To test the hypothesis the abstract meaning of education and income must be derived or operationalized. The concepts should be measured. Education could be measured by "years of school completed" or "highest degree completed" etc. Income could be measured by "hourly rate of pay" or "yearly salary" etc.
When a set of hypotheses are grouped together they become a type of conceptual framework. When a conceptual framework is complex and incorporates causality or explanation it is generally referred to as a theory. According to noted philosopher of science Carl Gustav Hempel "An adequate empirical interpretation turns a theoretical system into a testable theory: The hypothesis whose constituent terms have been interpreted become capable of test by reference to observable phenomena. Frequently the interpreted hypothesis will be derivative hypotheses of the theory; but their confirmation or disconfirmation by empirical data will then immediately strengthen or weaken also the primitive hypotheses from which they were derived."[13]
Hempel provides a useful metaphor that describes the relationship between a conceptual framework and the framework as it is observed and perhaps tested (interpreted framework). "The whole system floats, as it were, above the plane of observation and is anchored to it by rules of interpretation. These might be viewed as strings which are not part of the network but link certain points of the latter with specific places in the plane of observation. By virtue of those interpretative connections, the network can function as a scientific theory"[14] Hypotheses with concepts anchored in the plane of observation are ready to be tested. In "actual scientific practice the process of framing a theoretical structure and of interpreting it are not always sharply separated, since the intended interpretation usually guides the construction of the theoretician."[15] It is, however, "possible and indeed desirable, for the purposes of logical clarification, to separate the two steps conceptually."[15]
Statistical hypothesis testing

Main article: Statistical hypothesis testing
When a possible correlation or similar relation between phenomena is investigated, such as, for example, whether a proposed remedy is effective in treating a disease, that is, at least to some extent and for some patients, the hypothesis that a relation exists cannot be examined the same way one might examine a proposed new law of nature: in such an investigation a few cases in which the tested remedy shows no effect do not falsify the hypothesis. Instead, statistical tests are used to determine how likely it is that the overall effect would be observed if no real relation as hypothesized exists. If that likelihood is sufficiently small (e.g., less than 1%), the existence of a relation may be assumed. Otherwise, any observed effect may as well be due to pure chance.
In statistical hypothesis testing two hypotheses are compared, which are called the null hypothesis and the alternative hypothesis. The null hypothesis is the hypothesis that states that there is no relation between the phenomena whose relation is under investigation, or at least not of the form given by the alternative hypothesis. The alternative hypothesis, as the name suggests, is the alternative to the null hypothesis: it states that there is some kind of relation. The alternative hypothesis may take several forms, depending on the nature of the hypothesized relation; in particular, it can be two-sided (for example: there is some effect, in a yet unknown direction) or one-sided (the direction of the hypothesized relation, positive or negative, is fixed in advance).
Conventional significance levels for testing the hypotheses are .10, .05, and .01. Whether the null hypothesis is rejected and the alternative hypothesis is accepted, all must be determined in advance, before the observations are collected or inspected. If these criteria are determined later, when the data to be tested is already known, the test is invalid.[16]
It is important to mention that the above procedure is actually dependent on the number of the participants (units or sample size) that is included in the study. For instance, the sample size may be too small to reject a null hypothesis and, therefore, is recommended to specify the sample size from the beginning. It is advisable to define a small, medium and large effect size for each of a number of the important statistical tests which are used to test the hypotheses.

In its ancient usage, hypothesis also refers to a summary of the plot of a classical drama.
In Plato's Meno (86e–87b), Socrates dissects virtue with a method used by mathematicians,[2] that of "investigating from a hypothesis."[3] In this sense, 'hypothesis' refers to a clever idea or to a convenient mathematical approach that simplifies cumbersome calculations.[4] Cardinal Bellarmine gave a famous example of this usage 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.[5]
In common usage in the 21st century, a hypothesis refers to a provisional idea whose merit requires evaluation. For proper evaluation, the framer of a hypothesis needs to define specifics 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. Normally, scientific hypotheses have the form of a mathematical model. Sometimes, but not always, one can also formulate them as existential statements, stating that some particular instance of the phenomenon under examination 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 may also invoke statistics and only talk about probabilities. Karl Popper, following others, has argued that a hypothesis must be falsifiable, and that one cannot regard a proposition or theory as scientific if it does not admit the possibility of being shown false. Other philosophers of science have rejected the criterion of falsifiability or supplemented it with other criteria, such as verifiability (e.g., verificationism) or coherence (e.g., confirmation holism). The scientific method involves experimentation on the basis of hypotheses to answer questions and explore observations.
In framing a hypothesis, the investigator must not currently know the outcome of a test or that it remains reasonably under continuing investigation. Only in such cases does the experiment, test or study potentially increase the probability of showing the truth of a hypothesis. If the researcher already knows the outcome, it counts as a "consequence" — and the researcher should have already considered this while formulating the hypothesis. If one cannot assess the predictions by observation or by experience, the hypothesis classes as 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.
Scientific hypothesis

People refer to a trial solution to a problem as a hypothesis, often called an "educated guess"[6][7] because it provides a suggested solution based on the evidence. Experimenters may test and reject several hypotheses before solving the problem.
According to Schick and Vaughn,[8] researchers weighing up alternative hypotheses may take into consideration:

• Testability (compare falsifiability as discussed above)
• 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
• Conservatism – the degree of "fit" with existing recognized knowledge-systems.

Working hypothesis

Main article: Working hypothesis
A 'working hypothesis' is a hypothesis that is provisionally accepted as a basis for further research[9] in the hope that a tenable theory will be produced, even if the hypothesis ultimately fails.[10] Like all hypotheses, a working hypothesis is constructed as a statement of expectations, which can be linked to the exploratory research purpose in empirical investigation and are often used as a conceptual framework in qualitative research.[11][12]
In recent years, philosophers of science have tried to integrate the various approaches to evaluating hypotheses, and the scientific method in general, to form a more complete system that integrates the individual concerns of each approach. Notably, Imre Lakatos and Paul Feyerabend, respectively a colleague and a student, have produced novel attempts at such a synthesis.
Hypotheses, concepts and measurement

Concepts, as abstract units of meaning, play a key role in the development and testing of hypotheses. Concepts are the basic components of hypotheses. Most formal hypotheses connect concepts by specifying the expected relationships between concepts. For example, a simple relational hypothesis such as "education increases income" specifies a positive relationship between the concepts "education" and "income." This abstract or conceptual hypothesis cannot be tested. First, it must be operationalized or situated in the real world by rules of interpretation. Consider again the simple hypothesis "Education increases Income." To test the hypothesis the abstract meaning of education and income must be derived or operationalized. The concepts should be measured. Education could be measured by "years of school completed" or "highest degree completed" etc. Income could be measured by "hourly rate of pay" or "yearly salary" etc.
When a set of hypotheses are grouped together they become a type of conceptual framework. When a conceptual framework is complex and incorporates causality or explanation it is generally referred to as a theory. According to noted philosopher of science Carl Gustav Hempel "An adequate empirical interpretation turns a theoretical system into a testable theory: The hypothesis whose constituent terms have been interpreted become capable of test by reference to observable phenomena. Frequently the interpreted hypothesis will be derivative hypotheses of the theory; but their confirmation or disconfirmation by empirical data will then immediately strengthen or weaken also the primitive hypotheses from which they were derived."[13]
Hempel provides a useful metaphor that describes the relationship between a conceptual framework and the framework as it is observed and perhaps tested (interpreted framework). "The whole system floats, as it were, above the plane of observation and is anchored to it by rules of interpretation. These might be viewed as strings which are not part of the network but link certain points of the latter with specific places in the plane of observation. By virtue of those interpretative connections, the network can function as a scientific theory"[14] Hypotheses with concepts anchored in the plane of observation are ready to be tested. In "actual scientific practice the process of framing a theoretical structure and of interpreting it are not always sharply separated, since the intended interpretation usually guides the construction of the theoretician."[15] It is, however, "possible and indeed desirable, for the purposes of logical clarification, to separate the two steps conceptually."[15]
Statistical hypothesis testing

Main article: Statistical hypothesis testing
When a possible correlation or similar relation between phenomena is investigated, such as, for example, whether a proposed remedy is effective in treating a disease, that is, at least to some extent and for some patients, the hypothesis that a relation exists cannot be examined the same way one might examine a proposed new law of nature: in such an investigation a few cases in which the tested remedy shows no effect do not falsify the hypothesis. Instead, statistical tests are used to determine how likely it is that the overall effect would be observed if no real relation as hypothesized exists. If that likelihood is sufficiently small (e.g., less than 1%), the existence of a relation may be assumed. Otherwise, any observed effect may as well be due to pure chance.
In statistical hypothesis testing two hypotheses are compared, which are called the null hypothesis and the alternative hypothesis. The null hypothesis is the hypothesis that states that there is no relation between the phenomena whose relation is under investigation, or at least not of the form given by the alternative hypothesis. The alternative hypothesis, as the name suggests, is the alternative to the null hypothesis: it states that there is some kind of relation. The alternative hypothesis may take several forms, depending on the nature of the hypothesized relation; in particular, it can be two-sided (for example: there is some effect, in a yet unknown direction) or one-sided (the direction of the hypothesized relation, positive or negative, is fixed in advance).
Conventional significance levels for testing the hypotheses are .10, .05, and .01. Whether the null hypothesis is rejected and the alternative hypothesis is accepted, all must be determined in advance, before the observations are collected or inspected. If these criteria are determined later, when the data to be tested is already known, the test is invalid.[16]
It is important to mention that the above procedure is actually dependent on the number of the participants (units or sample size) that is included in the study. For instance, the sample size may be too small to reject a null hypothesis and, therefore, is recommended to specify the sample size from the beginning. It is advisable to define a small, medium and large effect size for each of a number of the important statistical tests which are used to test the hypotheses.

inspirational thread music [video=youtube;2pCpXCabtXw]http://www.youtube.com/watch?v=2pCpXCabtXw&feature=related[/video]

Experiment is the step in the scientific method that arbitrates between competing models or hypotheses.[1][2] Experimentation is also used to test existing theories or new hypotheses in order to support them or disprove them.[3][4] An experiment or test can be carried out using the scientific method to answer a question or investigate a problem. First an observation is made. Then a question is asked, or a problem arises. Next, a hypothesis is formed. Then experiment is used to test that hypothesis. The results are analyzed, a conclusion is drawn, sometimes a theory is formed, and results are communicated through research papers.
A good experiment usually tests a hypothesis. However, an experiment may also test a question or test previous results. It is important that one knows all factors in an experiment. It is also important that the results are as accurate as possible. If an experiment is carefully conducted, the results usually either support or disprove the hypothesis. An experiment can never "prove" a hypothesis, it can only add support. However, one repeatable experiment that provides a counterexample can disprove a theory or hypothesis. An experiment must also control the possible confounding factors -- any factors that would mar the accuracy or repeatability of the experiment or the ability to interpret the results.
Francis Bacon was an English philosopher and scientist in the 17th century and an early and influential supporter of experimental science. He disagreed with the method of answering scientific questions by deduction and described it as follows: "Having first determined the question according to his will, man then resorts to experience, and bending her to conformity with his placets, leads her about like a captive in a procession."[5] Bacon wanted a method that relied on repeatable observations, or experiments. He was notably the first to order the scientific method as we understand it today.
When the problem or conditions do not permit a controlled experiment, such as in astronomical research, observational studies can be useful. For example, Tycho Brahe made careful observations and recorded measurements of stellar and planetary positions over time, which supported the Copernican theory of orbits and disproved Brahe's own hypothesis. After his death, Brahe's measurements proved useful in the development of Johannes Kepler's laws of planetary motion.
In the centuries that followed, important advances and discoveries were made by people who applied the scientific method in different areas. For example, Galileo Galilei was able to accurately measure time and experiment to make accurate measurements and conclusions about the speed of a falling body.Antoine Lavoisier was a French chemist in the late 1700s who used experiment to describe new areas such as combustion and biochemistry and to develop the theory of conservation of mass (matter).[7] During the 1800s, Louis Pasteur used the scientific method to disprove the prevailing theory of spontaneous generation and to develop the germ theory of disease.[8] Because of the importance of controlling potentially confounding variables, the use of well-designed laboratory experiments is preferred when possible.
[edit] Galileo Galilei


Measured fall time of a small steel sphere falling from various heights. The data is in good agreement with the predicted fall time of , where h is the height and g is the acceleration of gravity.


Galileo Galilei was a scientist who performed many quantitative experiments addressing many topics. Using several different methods, Galileo was able to accurately measure time. Previously, most scientists had used distance to describe falling bodies using geometry, which had been used and trusted since Euclid.[9] Galileo himself used geometrical methods to express his results. Galileo's successes were aided by the development of a new mathematics as well as cleverly designed experiments and equipment. At that time, another kind of mathematics was being developed—algebra. Algebra allowed arithmetical calculations to become as sophisticated as geometric ones. Algebra also allowed the discoveries of scientists such as Galileo—as well as later scientists like Newton, Maxwell and Einstein—to be later summarized by mathematical equations. These equations described physical relationships in a precise, self-consistent manner.
One prominent example is the "ball and ramp experiment."[10] In this experiment Galileo used an inclined plane and several steel balls of different weights. With this design, Galileo was able to slow down the falling motion and record, with reasonable accuracy, the times at which a steel ball passed certain markings on a beam.[11] Galileo disproved Aristotle's assertion that weight affects the speed of an object's fall. According to Aristotle's Theory of Falling Bodies, the heavier steel ball would reach the ground before the lighter steel ball. Galileo's hypothesis was that the two balls would reach the ground at the same time.
Other than Galileo, not many people of his day were able to accurately measure short time periods, such as the fall time of an object. Galileo accurately measured these short periods of time by creating a pulsilogon. This was a machine created to measure time using a pendulum.[12] The pendulum was synchronized to the human pulse. He used this to measure the time at which the weighted balls passed marks that he had made on the inclined plane. He measured to find that balls of different weights reached the bottom of the inclined plane at the same time and that the distance traveled was proportional to the square of the elapsed time.[13] Later scientists summarized Galileo's results as The Equation of Falling Bodies.[14][15]
Distance d traveled by an object falling for time t where g is gravitational acceleration (~ 9.8 m/s2):

These results supported Galileo's hypothesis that objects of different weights, when measured at the same point in their fall, are falling at the same speed because they experience the same gravitational acceleration.
[edit] Antoine Lavoisier


Lavoisier and Laplace's ice calorimeter device


Antoine Lavoisier (1743–1794) was a French chemist regarded as the founder of modern chemistry. Lavoisier's experiments were among the first truly quantitative chemical experiments. He showed that, although matter changes its state in a chemical reaction, the quantity of matter is the same at the end as at the beginning of every chemical reaction. In one experiment, he burned phosphorus and sulfur in air to see whether the results further supported his previous conclusion (Law of Conservation of Mass). In this experiment, however, he determined that the products weighed more than the original phosphorus and sulfur. He decided to do the experiment again. This time he measured the mass of the air surrounding the experiment as well. He discovered that the mass gained in the product was lost from the air. These experiments provided further support for his Law of Conservation of Mass.
One of Lavoisier's experiments connected the worlds of respiration and combustion. Lavoisier's hypothesis was that combustion and respiration were one and the same, and combustion occurs with every instance of respiration. Lavoisier, working with Pierre-Simon Laplace, designed an ice calorimeter apparatus for measuring the amount of heat given off during combustion or respiration. This machine consisted of three concentric compartments. The center compartment held the source of heat, in this case, the guinea pig or piece of burning charcoal. The middle compartment held a specific amount of ice for the heat source to melt. The outside compartment contained packed snow for insulation. Lavoisier then measured the quantity of carbon dioxide and the quantity of heat produced by confining a live guinea pig in this apparatus. Lavoisier also measured the heat and carbon dioxide produced when burning a piece of charcoal in the calorimeter. Using this data, he concluded that respiration was in fact a slow combustion process. He also discovered through precise measurements that these processes produced carbon dioxide and heat with the same constant of proportionality. He found that for 224 grains of "fixed air" (CO2) produced, 13 oz (370 g). of ice was melted in the calorimeter. Converting grains to grams and using the energy required to melt 13 oz (370 g). of ice, one can compute that for each gram of CO2 produced, about 2.02 kcal of energy was produced by the combustion of carbon or by respiration in Lavoisier's calorimeter experiments. This compares well with the modern published heat of combustion for carbon of 2.13 kcal/g.[16] This continuous slow combustion, which Lavoisier and Laplace supposed took place in the lungs, enabled the living animal to maintain its body temperature above that of its surroundings, thus accounting for the puzzling phenomenon of animal heat.[17] Lavoisier concluded, "Lla respiration est donc une combustion," That is, respiratory gas exchange is combustion, like that of burning a candle.
Lavoisier was the first to conclude by experiment that the Law of Conservation of Mass applied to chemical change.[18] His hypothesis was that the mass of the reactants would be the same as the mass of the products in a chemical reaction. He experimented on vinous fermentation. He determined the amounts of hydrogen, oxygen, and carbon in sugar. He weighed a quantity of sugar, added yeast and water in measured amounts, and allowed the mixture to ferment. Lavoisier measured the mass of the carbonic acid gas and water that were given off during fermentation and weighed the residual liquor, the components of which were then separated and analyzed to determine their elementary composition.[19] In this way he controlled a couple of potential confounding factors. He was able to capture the carbonic acid gas and water vapor that were given off during fermentation so that his final measurements would be as accurate as possible. Lavoisier then concluded that the total mass of the reactants was equal to the mass of the final product and residue.[20] Moreover, he showed that the total mass of each constituent element before and after the chemical change remained the same. Similarly, he demonstrated via experimentation that the mass of products of combustion is equal to the mass of the reacting ingredients.
[edit] Louis Pasteur


(Above) A swannecked flask is pictured with broth and no bacteria in it after being boiled to remove any germs or microorganisms. (Below) Another swannecked flask is pictured with outside contaminants in the air allowed in. This broth grows microorganisms.


Louis Pasteur, regarded as the "Father of Microbiological sciences and immunology," was a French biologist during the 19th century.[21] He discovered and supported by experimental results the idea that disease-causing agents do not spontaneously appear but are alive and need the right environment to prosper and multiply. Stemming from this discovery, he used experiment to develop vaccines for chicken cholera, anthrax and rabies, and to develop methods for reducing bacteria in some food products by heating them (pasteurization). His work also led him to advocate (along with the English physician Dr. Joseph Lister) for antiseptic surgical techniques. Most scientists of that day believed that microscopic life sprang into existence from nonliving matter. This idea was called spontaneous generation.
Pasteur's observations of tiny organisms under the microscope caused him to doubt spontaneous generation. He designed an experiment to test it. His hypothesis was that life could not arise from where there is no life. He took care to control possible confounding factors. For example, he needed to make sure there was no life, even microscopic, in the flasks of broth he used as a test medium. He decided to kill any microscopic organisms already present by boiling the broth until he was confident that any microorganisms present were killed. Pasteur also needed to make sure that no microscopic organisms entered the broth after boiling, yet the broth needed exposure to air to properly test the theory. A colleague suggested a flask with a neck the shape of an "S" turned sideways. Dust (which Pasteur thought contained microorganisms) would be trapped at the bottom of the first curve, but the air would flow freely through.[22]
Thus, if bacteria should really be spontaneously generated, then they should be growing in the flask after a few days. If spontaneous generation did not occur, then the contents of the flasks would remain lifeless. In the end, it was a complete success; not a single microorganism appeared in the broth. Then Pasteur allowed the dust containing the microorganisms to mix with the broth. In just a few days the broth became cloudy from millions of organisms growing in it. For two more years, he repeated the experiment in various conditions and locales to assure himself that the results were correct. In this way Pasteur supported his hypothesis that spontaneous generation does not occur.[23] Despite the experimental results supporting his hypotheses and his success curing or preventing various diseases, correcting the public misconception of spontaneous generation was a slow, difficult process.
As he worked to solve specific problems, Pasteur's notions were sometimes corrected by the results of his experiments, such as when he was asked to find the cause of disease devastating the French silkworm industry in 1865. After a year of diligent work he correctly identified a culprit organism and gave practical advice for developing a healthy population of moths. However, when he tested his own advice, he found disease still present. It turned out he had been correct but incomplete – there were two organisms at work. It took two more years of experimenting to find the complete solution.[24]
[edit] Observational science

Observational science is used when it is impractical to fit a system into a laboratory setting. It can also be used when confounding factors are either limited or known well enough to analyze the data in light of them. In order for an observational science to be valid, the confounding factors must be known and accounted for.
[edit] Astronomy

One such observational scientist was Tycho Brahe. Brahe's observations of stellar and planetary positions were noteworthy both for their accuracy and quantity.[25] His celestial positions were much more accurate than those of any predecessor or contemporary. In an observatory funded for him by King Frederick II of Denmark, Brahe built the largest observing instruments yet constructed.[26] Because of the large size of this equipment, Brahe was able to measure angles to an accuracy of better than 0.1 degree. This was more accurate than any previous observations, and close to the limit that the human eye can observe.[27] In this way, Brahe was able to make observations about stellar and planetary positions in a lab setup.
Brahe himself was not a Copernican but proposed a system in which the Sun and Moon orbited the Earth, while the other planets orbited the Sun. His system provided a safe position for astronomers who were dissatisfied with older models but were reluctant to accept the Earth's motion. It gained a considerable following after 1616 when Rome decided officially that the heliocentric model was contrary to both philosophy and Scripture, and could be discussed only as a computational convenience that had no connection to fact. His system also offered a major innovation: while both the geocentric model and the heliocentric model as set forth by Copernicus relied on the idea of transparent rotating crystalline spheres to carry the planets in their orbits, Brahe eliminated the spheres entirely.
Johannes Kepler used the accurate observations of Brahe to discover the shape of Mars's orbit. His first hypothesis was that the orbit was circular. After four years of research and testing 70 different combinations of circles and epicycles, he devised a shape that would fit Mars's orbit. However, the model was accurate to only 0.13 degrees.[28] Kepler knew that Brahe's observations could be used to develop an orbit shape more accurate than this. Kepler eventually decided to try various oval shaped orbits. This implied that the speed of the planet changed as it traveled around the oval. After nine years, he found that elliptical orbits fit satisfactorily with the observed path of Mars. He found that this shape worked not only for Mars, but also for every planet that Brahe had observed.[28]
[edit] Biology

Observational studies are not experiments. By definition, observational studies lack the manipulation required for Baconian experiments. In addition, observational studies in biological systems often involve variables that are challenges to quantify or control. Nevertheless, observational studies are used because it is sometimes too difficult (too expensive, or too much time required) or unethical to conduct longitudinal experiments with human or animal subjects. In these situations, observational studies have value because they often suggest hypotheses that can be tested with randomized experiments or by collecting fresh data.
In providing therapies for human subjects, for example in psychology or health care, it is unethical to provide a substandard treatment to patients. Therefore, ethical review boards are supposed to stop clinical trials and other experiments unless a new treatment is believed to offer benefits as good as current best practice.[29] It is also unethical and often illegal to conduct randomized experiments on the effects of substandard or harmful treatments, such as the effects of ingesting arsenic on human health. To understand the effects of such exposures, scientists use observational studies.
Observational studies are limited because they lack the statistical properties of randomized experiments. In a randomized experiment, the method of randomization specified in the experimental protocol guides the statistical analysis, which is usually specified also by the experimental protocol.[30] Without a statistical model that reflects an objective randomization, the statistical analysis relies on a subjective model.[30] Inferences from subjective models are unreliable in theory and practice.[31] In fact, there are several cases where carefully conducted observational studies consistently give wrong results, that is, where the results of the observational studies are inconsistent and also differ from the results of experiments. For example, epidemiological studies of colon cancer consistently show beneficial correlations with broccoli consumption, while experiments find no benefit.[32]
A particular problem with observational studies involving human subjects is the great difficulty attaining fair comparisons between treatments (or exposures), because such studies are prone to selection bias, and groups receiving different treatments (exposures) may differ greatly according to their covariates (age, height, weight, medications, exercise, nutritional status, ethnicity, family medical history, etc.). In contrast, randomization implies that for each covariate, the mean for each group is expected to be the same. For any randomized trial, some variation from the mean is expected, of course, but the randomization ensures that the experimental groups have mean values that are close, due to the central limit theorem and Markov's inequality. With poor randomization, the systematic variation in covariates between the treatment groups (or exposure groups) makes it difficult to separate the effect of the treatment (exposure) from the effects of the other covariates, most of which have not been measured. The mathematical models used to analyze such data must consider each differing covariate (if measured), and the results will not be meaningful if a covariate is neither randomized nor included in the model.
To avoid these conditions that render an experiment far less useful, physicians conducting medical trials, say for U.S. Food and Drug Administration approval, will quantify and randomize the covariates that can be identified. Researchers attempt to reduce the biases of observational studies with complicated statistical methods such as propensity score matching methods, which require large populations of subjects and extensive information on covariates. Outcomes are also quantified when possible (bone density, amount of some cell or substance in the blood, physical strength or endurance, etc.) and not based on a subject's or a professional observer's opinion. In this way, the design of an observational study can render the results more objective and therefore more convincing.
See also: Hierarchy of evidence and Quasi-empirical methods
[edit] Natural experiments

Main article: Natural experiment
The term "experiment" usually implies a controlled experiment, but sometimes controlled experiments are prohibitively difficult or impossible. In this case researchers resort to natural experiments or quasi-experiments. Natural experiments rely solely on observations of the variables of the system under study, rather than manipulation of just one or a few variables as occurs in controlled experiments. To the degree possible, they attempt to collect data for the system in such a way that contribution from all variables can be determined, and where the effects of variation in certain variables remain 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 approach the power of controlled experiments. Usually, however, there is some correlation between these variables, which reduces the reliability of natural experiments relative to what could be concluded if a controlled experiment were performed. Also, because natural experiments usually take place in uncontrolled environments, variables from undetected sources are neither measured nor held constant, and these may produce illusory correlations in variables under study.
Much research in several important science disciplines, including economics, political science, geology, paleontology, ecology, meteorology, and astronomy, relies on quasi-experiments. For example, in astronomy it is clearly impossible, when testing the hypothesis "suns are collapsed clouds of hydrogen", to start out with a giant cloud of hydrogen, and then perform the experiment of waiting a few billion years for it to form a sun. However, by observing various clouds of hydrogen in various states of collapse, and other implications of the hypothesis (for example, the presence of various spectral emissions from the light of stars), we can collect data we require to support the hypothesis. An early example of this type of experiment was the first verification in the 17th century that light does not travel from place to place instantaneously, but instead has a measurable speed. Observation of the appearance of the moons of Jupiter were slightly delayed when Jupiter was farther from Earth, as opposed to when Jupiter was closer to Earth; and this phenomenon was used to demonstrate that the difference in the time of appearance of the moons was consistent with a measurable speed.
[edit] Field experiments

Main article: Field experiment
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.

Experiment is the step in the scientific method that arbitrates between competing models or hypotheses.[1][2] Experimentation is also used to test existing theories or new hypotheses in order to support them or disprove them.[3][4] An experiment or test can be carried out using the scientific method to answer a question or investigate a problem. First an observation is made. Then a question is asked, or a problem arises. Next, a hypothesis is formed. Then experiment is used to test that hypothesis. The results are analyzed, a conclusion is drawn, sometimes a theory is formed, and results are communicated through research papers.
A good experiment usually tests a hypothesis. However, an experiment may also test a question or test previous results. It is important that one knows all factors in an experiment. It is also important that the results are as accurate as possible. If an experiment is carefully conducted, the results usually either support or disprove the hypothesis. An experiment can never "prove" a hypothesis, it can only add support. However, one repeatable experiment that provides a counterexample can disprove a theory or hypothesis. An experiment must also control the possible confounding factors -- any factors that would mar the accuracy or repeatability of the experiment or the ability to interpret the results.
Francis Bacon was an English philosopher and scientist in the 17th century and an early and influential supporter of experimental science. He disagreed with the method of answering scientific questions by deduction and described it as follows: "Having first determined the question according to his will, man then resorts to experience, and bending her to conformity with his placets, leads her about like a captive in a procession."[5] Bacon wanted a method that relied on repeatable observations, or experiments. He was notably the first to order the scientific method as we understand it today.
When the problem or conditions do not permit a controlled experiment, such as in astronomical research, observational studies can be useful. For example, Tycho Brahe made careful observations and recorded measurements of stellar and planetary positions over time, which supported the Copernican theory of orbits and disproved Brahe's own hypothesis. After his death, Brahe's measurements proved useful in the development of Johannes Kepler's laws of planetary motion.
In the centuries that followed, important advances and discoveries were made by people who applied the scientific method in different areas. For example, Galileo Galilei was able to accurately measure time and experiment to make accurate measurements and conclusions about the speed of a falling body.Antoine Lavoisier was a French chemist in the late 1700s who used experiment to describe new areas such as combustion and biochemistry and to develop the theory of conservation of mass (matter).[7] During the 1800s, Louis Pasteur used the scientific method to disprove the prevailing theory of spontaneous generation and to develop the germ theory of disease.[8] Because of the importance of controlling potentially confounding variables, the use of well-designed laboratory experiments is preferred when possible.
[edit] Galileo Galilei


Measured fall time of a small steel sphere falling from various heights. The data is in good agreement with the predicted fall time of , where h is the height and g is the acceleration of gravity.


Galileo Galilei was a scientist who performed many quantitative experiments addressing many topics. Using several different methods, Galileo was able to accurately measure time. Previously, most scientists had used distance to describe falling bodies using geometry, which had been used and trusted since Euclid.[9] Galileo himself used geometrical methods to express his results. Galileo's successes were aided by the development of a new mathematics as well as cleverly designed experiments and equipment. At that time, another kind of mathematics was being developed—algebra. Algebra allowed arithmetical calculations to become as sophisticated as geometric ones. Algebra also allowed the discoveries of scientists such as Galileo—as well as later scientists like Newton, Maxwell and Einstein—to be later summarized by mathematical equations. These equations described physical relationships in a precise, self-consistent manner.
One prominent example is the "ball and ramp experiment."[10] In this experiment Galileo used an inclined plane and several steel balls of different weights. With this design, Galileo was able to slow down the falling motion and record, with reasonable accuracy, the times at which a steel ball passed certain markings on a beam.[11] Galileo disproved Aristotle's assertion that weight affects the speed of an object's fall. According to Aristotle's Theory of Falling Bodies, the heavier steel ball would reach the ground before the lighter steel ball. Galileo's hypothesis was that the two balls would reach the ground at the same time.
Other than Galileo, not many people of his day were able to accurately measure short time periods, such as the fall time of an object. Galileo accurately measured these short periods of time by creating a pulsilogon. This was a machine created to measure time using a pendulum.[12] The pendulum was synchronized to the human pulse. He used this to measure the time at which the weighted balls passed marks that he had made on the inclined plane. He measured to find that balls of different weights reached the bottom of the inclined plane at the same time and that the distance traveled was proportional to the square of the elapsed time.[13] Later scientists summarized Galileo's results as The Equation of Falling Bodies.[14][15]
Distance d traveled by an object falling for time t where g is gravitational acceleration (~ 9.8 m/s2):

These results supported Galileo's hypothesis that objects of different weights, when measured at the same point in their fall, are falling at the same speed because they experience the same gravitational acceleration.
[edit] Antoine Lavoisier


Lavoisier and Laplace's ice calorimeter device


Antoine Lavoisier (1743–1794) was a French chemist regarded as the founder of modern chemistry. Lavoisier's experiments were among the first truly quantitative chemical experiments. He showed that, although matter changes its state in a chemical reaction, the quantity of matter is the same at the end as at the beginning of every chemical reaction. In one experiment, he burned phosphorus and sulfur in air to see whether the results further supported his previous conclusion (Law of Conservation of Mass). In this experiment, however, he determined that the products weighed more than the original phosphorus and sulfur. He decided to do the experiment again. This time he measured the mass of the air surrounding the experiment as well. He discovered that the mass gained in the product was lost from the air. These experiments provided further support for his Law of Conservation of Mass.
One of Lavoisier's experiments connected the worlds of respiration and combustion. Lavoisier's hypothesis was that combustion and respiration were one and the same, and combustion occurs with every instance of respiration. Lavoisier, working with Pierre-Simon Laplace, designed an ice calorimeter apparatus for measuring the amount of heat given off during combustion or respiration. This machine consisted of three concentric compartments. The center compartment held the source of heat, in this case, the guinea pig or piece of burning charcoal. The middle compartment held a specific amount of ice for the heat source to melt. The outside compartment contained packed snow for insulation. Lavoisier then measured the quantity of carbon dioxide and the quantity of heat produced by confining a live guinea pig in this apparatus. Lavoisier also measured the heat and carbon dioxide produced when burning a piece of charcoal in the calorimeter. Using this data, he concluded that respiration was in fact a slow combustion process. He also discovered through precise measurements that these processes produced carbon dioxide and heat with the same constant of proportionality. He found that for 224 grains of "fixed air" (CO2) produced, 13 oz (370 g). of ice was melted in the calorimeter. Converting grains to grams and using the energy required to melt 13 oz (370 g). of ice, one can compute that for each gram of CO2 produced, about 2.02 kcal of energy was produced by the combustion of carbon or by respiration in Lavoisier's calorimeter experiments. This compares well with the modern published heat of combustion for carbon of 2.13 kcal/g.[16] This continuous slow combustion, which Lavoisier and Laplace supposed took place in the lungs, enabled the living animal to maintain its body temperature above that of its surroundings, thus accounting for the puzzling phenomenon of animal heat.[17] Lavoisier concluded, "Lla respiration est donc une combustion," That is, respiratory gas exchange is combustion, like that of burning a candle.
Lavoisier was the first to conclude by experiment that the Law of Conservation of Mass applied to chemical change.[18] His hypothesis was that the mass of the reactants would be the same as the mass of the products in a chemical reaction. He experimented on vinous fermentation. He determined the amounts of hydrogen, oxygen, and carbon in sugar. He weighed a quantity of sugar, added yeast and water in measured amounts, and allowed the mixture to ferment. Lavoisier measured the mass of the carbonic acid gas and water that were given off during fermentation and weighed the residual liquor, the components of which were then separated and analyzed to determine their elementary composition.[19] In this way he controlled a couple of potential confounding factors. He was able to capture the carbonic acid gas and water vapor that were given off during fermentation so that his final measurements would be as accurate as possible. Lavoisier then concluded that the total mass of the reactants was equal to the mass of the final product and residue.[20] Moreover, he showed that the total mass of each constituent element before and after the chemical change remained the same. Similarly, he demonstrated via experimentation that the mass of products of combustion is equal to the mass of the reacting ingredients.
[edit] Louis Pasteur


(Above) A swannecked flask is pictured with broth and no bacteria in it after being boiled to remove any germs or microorganisms. (Below) Another swannecked flask is pictured with outside contaminants in the air allowed in. This broth grows microorganisms.


Louis Pasteur, regarded as the "Father of Microbiological sciences and immunology," was a French biologist during the 19th century.[21] He discovered and supported by experimental results the idea that disease-causing agents do not spontaneously appear but are alive and need the right environment to prosper and multiply. Stemming from this discovery, he used experiment to develop vaccines for chicken cholera, anthrax and rabies, and to develop methods for reducing bacteria in some food products by heating them (pasteurization). His work also led him to advocate (along with the English physician Dr. Joseph Lister) for antiseptic surgical techniques. Most scientists of that day believed that microscopic life sprang into existence from nonliving matter. This idea was called spontaneous generation.
Pasteur's observations of tiny organisms under the microscope caused him to doubt spontaneous generation. He designed an experiment to test it. His hypothesis was that life could not arise from where there is no life. He took care to control possible confounding factors. For example, he needed to make sure there was no life, even microscopic, in the flasks of broth he used as a test medium. He decided to kill any microscopic organisms already present by boiling the broth until he was confident that any microorganisms present were killed. Pasteur also needed to make sure that no microscopic organisms entered the broth after boiling, yet the broth needed exposure to air to properly test the theory. A colleague suggested a flask with a neck the shape of an "S" turned sideways. Dust (which Pasteur thought contained microorganisms) would be trapped at the bottom of the first curve, but the air would flow freely through.[22]
Thus, if bacteria should really be spontaneously generated, then they should be growing in the flask after a few days. If spontaneous generation did not occur, then the contents of the flasks would remain lifeless. In the end, it was a complete success; not a single microorganism appeared in the broth. Then Pasteur allowed the dust containing the microorganisms to mix with the broth. In just a few days the broth became cloudy from millions of organisms growing in it. For two more years, he repeated the experiment in various conditions and locales to assure himself that the results were correct. In this way Pasteur supported his hypothesis that spontaneous generation does not occur.[23] Despite the experimental results supporting his hypotheses and his success curing or preventing various diseases, correcting the public misconception of spontaneous generation was a slow, difficult process.
As he worked to solve specific problems, Pasteur's notions were sometimes corrected by the results of his experiments, such as when he was asked to find the cause of disease devastating the French silkworm industry in 1865. After a year of diligent work he correctly identified a culprit organism and gave practical advice for developing a healthy population of moths. However, when he tested his own advice, he found disease still present. It turned out he had been correct but incomplete – there were two organisms at work. It took two more years of experimenting to find the complete solution.[24]
[edit] Observational science

Observational science is used when it is impractical to fit a system into a laboratory setting. It can also be used when confounding factors are either limited or known well enough to analyze the data in light of them. In order for an observational science to be valid, the confounding factors must be known and accounted for.
[edit] Astronomy

One such observational scientist was Tycho Brahe. Brahe's observations of stellar and planetary positions were noteworthy both for their accuracy and quantity.[25] His celestial positions were much more accurate than those of any predecessor or contemporary. In an observatory funded for him by King Frederick II of Denmark, Brahe built the largest observing instruments yet constructed.[26] Because of the large size of this equipment, Brahe was able to measure angles to an accuracy of better than 0.1 degree. This was more accurate than any previous observations, and close to the limit that the human eye can observe.[27] In this way, Brahe was able to make observations about stellar and planetary positions in a lab setup.
Brahe himself was not a Copernican but proposed a system in which the Sun and Moon orbited the Earth, while the other planets orbited the Sun. His system provided a safe position for astronomers who were dissatisfied with older models but were reluctant to accept the Earth's motion. It gained a considerable following after 1616 when Rome decided officially that the heliocentric model was contrary to both philosophy and Scripture, and could be discussed only as a computational convenience that had no connection to fact. His system also offered a major innovation: while both the geocentric model and the heliocentric model as set forth by Copernicus relied on the idea of transparent rotating crystalline spheres to carry the planets in their orbits, Brahe eliminated the spheres entirely.
Johannes Kepler used the accurate observations of Brahe to discover the shape of Mars's orbit. His first hypothesis was that the orbit was circular. After four years of research and testing 70 different combinations of circles and epicycles, he devised a shape that would fit Mars's orbit. However, the model was accurate to only 0.13 degrees.[28] Kepler knew that Brahe's observations could be used to develop an orbit shape more accurate than this. Kepler eventually decided to try various oval shaped orbits. This implied that the speed of the planet changed as it traveled around the oval. After nine years, he found that elliptical orbits fit satisfactorily with the observed path of Mars. He found that this shape worked not only for Mars, but also for every planet that Brahe had observed.[28]
[edit] Biology

Observational studies are not experiments. By definition, observational studies lack the manipulation required for Baconian experiments. In addition, observational studies in biological systems often involve variables that are challenges to quantify or control. Nevertheless, observational studies are used because it is sometimes too difficult (too expensive, or too much time required) or unethical to conduct longitudinal experiments with human or animal subjects. In these situations, observational studies have value because they often suggest hypotheses that can be tested with randomized experiments or by collecting fresh data.
In providing therapies for human subjects, for example in psychology or health care, it is unethical to provide a substandard treatment to patients. Therefore, ethical review boards are supposed to stop clinical trials and other experiments unless a new treatment is believed to offer benefits as good as current best practice.[29] It is also unethical and often illegal to conduct randomized experiments on the effects of substandard or harmful treatments, such as the effects of ingesting arsenic on human health. To understand the effects of such exposures, scientists use observational studies.
Observational studies are limited because they lack the statistical properties of randomized experiments. In a randomized experiment, the method of randomization specified in the experimental protocol guides the statistical analysis, which is usually specified also by the experimental protocol.[30] Without a statistical model that reflects an objective randomization, the statistical analysis relies on a subjective model.[30] Inferences from subjective models are unreliable in theory and practice.[31] In fact, there are several cases where carefully conducted observational studies consistently give wrong results, that is, where the results of the observational studies are inconsistent and also differ from the results of experiments. For example, epidemiological studies of colon cancer consistently show beneficial correlations with broccoli consumption, while experiments find no benefit.[32]
A particular problem with observational studies involving human subjects is the great difficulty attaining fair comparisons between treatments (or exposures), because such studies are prone to selection bias, and groups receiving different treatments (exposures) may differ greatly according to their covariates (age, height, weight, medications, exercise, nutritional status, ethnicity, family medical history, etc.). In contrast, randomization implies that for each covariate, the mean for each group is expected to be the same. For any randomized trial, some variation from the mean is expected, of course, but the randomization ensures that the experimental groups have mean values that are close, due to the central limit theorem and Markov's inequality. With poor randomization, the systematic variation in covariates between the treatment groups (or exposure groups) makes it difficult to separate the effect of the treatment (exposure) from the effects of the other covariates, most of which have not been measured. The mathematical models used to analyze such data must consider each differing covariate (if measured), and the results will not be meaningful if a covariate is neither randomized nor included in the model.
To avoid these conditions that render an experiment far less useful, physicians conducting medical trials, say for U.S. Food and Drug Administration approval, will quantify and randomize the covariates that can be identified. Researchers attempt to reduce the biases of observational studies with complicated statistical methods such as propensity score matching methods, which require large populations of subjects and extensive information on covariates. Outcomes are also quantified when possible (bone density, amount of some cell or substance in the blood, physical strength or endurance, etc.) and not based on a subject's or a professional observer's opinion. In this way, the design of an observational study can render the results more objective and therefore more convincing.
See also: Hierarchy of evidence and Quasi-empirical methods
[edit] Natural experiments

Main article: Natural experiment
The term "experiment" usually implies a controlled experiment, but sometimes controlled experiments are prohibitively difficult or impossible. In this case researchers resort to natural experiments or quasi-experiments. Natural experiments rely solely on observations of the variables of the system under study, rather than manipulation of just one or a few variables as occurs in controlled experiments. To the degree possible, they attempt to collect data for the system in such a way that contribution from all variables can be determined, and where the effects of variation in certain variables remain 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 approach the power of controlled experiments. Usually, however, there is some correlation between these variables, which reduces the reliability of natural experiments relative to what could be concluded if a controlled experiment were performed. Also, because natural experiments usually take place in uncontrolled environments, variables from undetected sources are neither measured nor held constant, and these may produce illusory correlations in variables under study.
Much research in several important science disciplines, including economics, political science, geology, paleontology, ecology, meteorology, and astronomy, relies on quasi-experiments. For example, in astronomy it is clearly impossible, when testing the hypothesis "suns are collapsed clouds of hydrogen", to start out with a giant cloud of hydrogen, and then perform the experiment of waiting a few billion years for it to form a sun. However, by observing various clouds of hydrogen in various states of collapse, and other implications of the hypothesis (for example, the presence of various spectral emissions from the light of stars), we can collect data we require to support the hypothesis. An early example of this type of experiment was the first verification in the 17th century that light does not travel from place to place instantaneously, but instead has a measurable speed. Observation of the appearance of the moons of Jupiter were slightly delayed when Jupiter was farther from Earth, as opposed to when Jupiter was closer to Earth; and this phenomenon was used to demonstrate that the difference in the time of appearance of the moons was consistent with a measurable speed.
[edit] Field experiments

Main article: Field experiment
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.