what is natural experiment in research methodology?
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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.\n\nIn 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.\n\nFor 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.\n\nIn 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.\n\nAs 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".
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Natural Resouces are the physical things (minerals, water, forests, etc) that an area or country contains. hey dont affect the country...
29764
None really - science is a methodology.  The scientists might have faith in all sorts of things -but science itself does not.
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The researcher can use various statistical methods such as enlarging sample sizes, using peer review, utilizing up-to-date data to support researched claim, etc.\n\nBut if you aren't looking for an open-ended answer to an open-ended question (*grin*), the following answer should suffice:\n\n"Statistical methodology assists researchers in making inferences about a large group (a population) based on observations of a smaller subset of that group (a sample). Sources of bias are conditions or circumstances which affect the external validity of statistical results. Thus, in order for a researcher to make legitimate conclusions about the specified population, two characteristics must be present within the sample: representative sampling and valid statistical assumptions."
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Natural variations are variations that are often the result of random mutations, that arise when cells divide during development of new organisms. \nWhen these organisms reproduce, they pass on these variations to their offspring – inheritance of natural variation. \n\nOrganisms can vary in size, coloration, ability to fight off diseases and countless other traits. These natural variations and their ability to be inherited makes it  possible to survive as a species.
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Quantitative Research\n\nBy definition, measurement must be objective, quantitative and statistically valid. Simply put, it's about numbers, objective hard data. A scientifically calculated sample of people from a population is asked a set of questions on a survey to determine the frequency and percentage of their responses.
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Indecent proposal and the natural
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This question is basically nonsense....\n\nBut, an experiment is a test or series of tests designed to isolate, quantify, and clarify whether a preconceived idea or theory is correct.\n\nHow to define that using mathematics...haven't a clue.
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Steps of the scientific method\n\n1.  Name the problem or question\n\n2.  Form an educated guess (hypothesis)\n     of the cause of the problem and make\n     predictions based upon the hypothesis\n\n 3.  Test your hypothesis by doing an\n     experiment or study (with proper controls)\n\n 4.  Check and interpret your results\n\n 5.  Report your results to the scientific\n     community
702007
do the experiment and note what processes are taken (ex growing a plant-1st put seed in dirt 2nd water etc etc) this is analysis\n\ndid you take some steps during your experiment that may have caused some delays or errors (ex did you remember to give your plant lighting?)
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Test the Hypothesis\nThis is the part where the experimenter actually completes hands-on tests to either prove or disprove the hypothesis. To truly test the hypothesis, the experimenter must compare the results of the experimental group compared to a control group where the variables have not been changed. Example: For two weeks, a group of people eat a certain amount of chocolate each day and another group eats no chocolate.
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As the agricultural education profession works to expand its research base regarding teacher capacity to deliver scientific concepts effectively, the work completed in this area by our colleagues in science education should be examined.\nBasic Skills\nObserving Noting the properties of objects and situations using the five senses Classifying Relating objects and events according to their properties or attributes\nSpace/time relations Visualizing and manipulating objects and events, dealing with shapes, time, distance, and speed\nUsing numbers Using quantitative relationships Measuring Expressing the amount of an object or substance in quantitative terms Inferring Giving an explanation for a particular object or event Predicting Forecasting a future occurrence based on past observation or theextension of data\nIntegrated Skills\nDefining\noperationally\nDeveloping statements that present concrete descriptions of an object or event by telling one what to do or observe\nFormulating models Constructing images, objects, or mathematical formulas to explain ideas\nControlling variables Manipulating and controlling properties that relate to situations or events for the purpose of determining causation Interpreting data Arriving at explanations, inferences, or hypotheses from data that\nhave been graphed or placed in a table Hypothesizing Stating a tentative generalization of observations or inferences that\nmay be used to explain a relatively larger number of events but\nthat is subject to immediate or eventual testing by one or more experiments\nExperimenting Testing a hypothesis through the manipulation and control of independent variables and noting the effects on a dependent variable; interpreting and presenting results in the form of a report that others can follow to replicate the experiment science concept knowledge and performance of agriculture teachers. If not\nalready being done, effective science instruction methods should be infused into agriculture\nteaching methods courses as a way to bolster the scientific rigor of future agriculture teachers\nand simply to enhance their effectiveness.
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Science is an attempt to develop a set of tools that permit accurate predictions of natural phenomena.  It is a process of testing hypotheses and then tuning the hypotheses to match the observations made during the test, making new predictions with the hopefully improved hypotheses and testing them again.\n\nAll observations constitute a form of measurement (not necessarily quantitative measurement). Without measurement of observations, the cycle breaks and what would otherwise be science is merely random guessing. \n\nEven an "educated guess" relies on measurements made throughout ones life.  For instance, if I guess that if I let go of an orange it will fall, that relies on my experiences of observing things falling.  Even if I have never measured distances or acceleration or velocities or travel times of a falling object, I HAVE noted that "most of the time" things fall.  That is a measurement.\n\nQuantifiable measurements allow us to make predictions with greater precision. "This orange will hit the floor before you can close the door," for instance.   Numerical measurements can also help us see more complex relationships between observables that we might otherwise miss.  These allow us to extend hypotheses in ways we might not be able to.
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randomization is a myth, try doing something randomly and you have picked a path. observation is just that you would not have input to the study.
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What equipment?\nWhat experiment?\nWhat error?
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(getting out my lab report from last year) Ahem, An abstract tells about the purpose of the experiment and a shortened version of the procedure and results.\n\nFor example:\n\nThe purpose of this experiment was to find out which morning glory would grow best under the influence of different sodas (coca-cola, dr.pepper, orange soda, cream soda) in 30 days. I started out with 2 inch high morning glories.\n\nI gave each plant 25 milliliters of the specified soda each day. I measured the plant's growth in centimeters. This determined the growth rate of each plant.\n\nMy hypothesis was that cream soda would help the morning glories grow best.My data shows that my hypothesis should be rejected. Orange soda made the morning glories grow best.\n\nOrange Soda made the plant grow 12 centimeters, which was a larger amount than than the growth rate of the other sodas plants. I found that Cream Soda did the worst, only having a growth of 4 centimeters.\n------------------------------------------------------------------------------------\nThis is not real data, nor is the information even remotely correct.\nMind you, I got an A+ on my research report and I hope you do too. Remember, this is just an example of an abstract, and the data I put in IS NOT correct. Check with your teacher before you write your abstract.
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Great idea.  What is your hypothesis?  What experiment will you do?  What data will you collect?  How will you interpret it?