7 Advantages OF Experiment PDF

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7 ADVANTAGES OF EXPERIMENT 

1. It provides researchers with a high level of control. By being able to isolate specific variables, it becomes possible to determine if a potential outcome is viable. Each variable can be controlled on its own or in different combinations to study what possible outcomes are available for a product, theory, or idea as well. This provides a tremendous advantage in an ability to find accurate results.

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2. There is no limit to the subject matter or industry involved. Experimental research is not limited to a specific industry or type of idea. It can be used in a wide variety of situations. Teachers might use experimental research to determine if a new method of teaching or a new curriculum is better than an older system. Pharmaceutical companies use experimental research to determine the viability of a new product.

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3. Experimental research provides conclusions that are specific. Because experimental research provides such a high level of control, it can produce results that are specific and relevant with consistency. It is possible to determine success or failure, making it possible to understand the validity of a product, theory, or idea in a much shorter amount of time compared to other verification methods. You know the outcome of the research because you bring the variable to its conclusion.

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4. The results of experimental research can be duplicated. Experimental research is straightforward, basic form of research that allows for its duplication when the same variables are controlled by others. This helps to promote the validity of a concept for products, ideas, and theories. This allows anyone to be able to check and verify published results, which often allows for better results to be achieved, because the exact steps can produce the exact results.

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5. Natural settings can be replicated with faster speeds. When conducting research within a laboratory environment, it becomes possible to replicate conditions that could take a long time so that the variables can be tested appropriately. This allows researchers to have a greater control of the extraneous variables which may exist as well, limiting the unpredictability of nature as each variable is being carefully studied.

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6. Experimental research allows cause and effect to be determined. The manipulation of variables allows for researchers to be able to look at various cause-andeffect relationships that a product, theory, or idea can produce. It is a process which allows researchers to dig deeper into what is possible, showing how the various variable relationships can provide specific benefits. In return, a greater understanding of the specifics within the research can be understood, even if an understanding of why that relationship is present isn’t presented to the researcher.

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7. It can be combined with other research methods. This allows experimental research to be able to provide the scientific rigor that may be needed for the results to stand on their own. It provides the possibility of determining what may be best for a specific demographic or population while also offering a better transference than anecdotal research can typically provide.

To see what happens. At it’s simplest, an experiment is a way of answering questions of the form “What would happen if…?” Such experiments often are conducted simply out of curiosity. This sort of experiment teaches you something about how the system works that you couldn’t have learned through observation, it gives you a starting point for further investigation (e.g., you can develop a model and/or do followup experiments to explain what happened), and it can be of direct applied relevance (e.g., if you want to know what effect trampling has on a grassland you’re trying to conserve, go out and trample on randomlyselected bits of it). There are limitations to such experiments, of course. Because they’re conducted without any hypothesis in mind, they’re typically difficult or impossible to interpret in light of existing hypotheses. And on their own, they don’t provide a good foundation for generalization (e.g., would the experiment come out the same way if you repeated it under different conditions, or in a different system?) As a means of measurement. These experiments are conducted to measure the quantitative relationship between two variables. Feeding trials to measure the shape of a consumer’s functional response are a common example: you provide individual predators with different densities of prey, and then plot predator feeding rate as a function of prey density. These experiments are a good way of isolating the relationship between two variables. For instance, in nature a predator’s feeding rate will depend on lots of things besides prey density, including some things that are likely confounded with prey density, making it difficult or impossible to use observational data to reliably estimate the true shape of the predator’s functional response. Or, maybe prey density just doesn’t vary that much in nature, so in order to measure how predator feeding rate would vary if prey density were to vary (which of course it might in future), you need to experimentally create variation in prey density. This is an example of a general principle: in order to learn how natural systems work, we’re often forced to create unnatural conditions (i.e. conditions that don’t currently exist, and may never exist or have existed). Of course, the challenge with these experiments is to make sure that the controls needed to isolate the relationship of interest don’t also distort the relationship of interest. For instance, feeding trials conducted in small arenas are infamous for overestimating predator feeding rates because

prey have nowhere to hide, and because prey and predators behave differently in small arenas than they do in nature. To test theoretical predictions. Probably the most common sort of experiment reported in leading ecology journals. Again, often most usefully performed in tractable model systems**. But as Wootton and Pfister point out, these kinds of experiments, at least as commonly conducted and interpreted by ecologists, have serious limitations that aren’t widely recognized. For instance, testing the predictions of only a single ecological model, while ignoring the predictions of alternative models, prevents you from inferring much about the truth of your chosen model. If model 1 predicts that experiment A will produce outcome X, and you conduct experiment A and find outcome X, you can’t treat that as evidence for model 1 if alternative models 2, 3, and 4 also predict the same outcome. It’s for this reason that Platt (1964) developed his famous argument for “strong inference“, with its emphasis on lining up alternative hypotheses and conducting “crucial experiments” that distinguish between those hypotheses. There’s another limitation of experiments conducted to test theoretical predictions, which Wootton and Pfister don’t recognize, but which is wellillustrated by one of their own examples. Wootton and Pfister’s first example of an experiment testing a theoretical prediction is the experiment of Sousa (1979) testing the intermediate disturbance hypothesis (IDH). Which, as readers of this blog know, is a really, really unfortunate example. Experiments to test predictions are only as good as the predictions they purport to test. So if those predictions derive from a logically-flawed model that doesn’t actually predict what you think it predicts (as is the case for several prominent versions of the IDH), then there’s no way to infer anything about the model from the experiment. The experiment is shooting at the wrong target. Or, if the prediction actually “derives” from a vague or incompletely specified model, then the experiment isn’t really shooting at a single target at all–it’s shooting at some vaguely- or incompletely-specified family of targets (alternative models), and so allows only weak or vague inferences about those targets (this is what I think was going on in the case of Sousa 1979). One way to avoid such ill-aimed experiments is for experimenters to rely more on mathematical models and less on verbal models for hypothesis generation. But another way to avoid such ill-aimed experiments is to

quit focusing so much on testing predictions and instead conduct an experiment… To test theoretical assumptions. It is quite commonly the case in ecology that different alternative models will make many similar predictions. For instance, models with and without selection (non-neutral and neutral models) infamously make the same predictions about many features of ecological and evolutionary systems. This makes it difficult to distinguish models by testing their predictions. So why not test their assumptions instead, thereby revealing which alternative model makes the right prediction for the right reasons, and which alternative is merely getting lucky and making the right prediction for the wrong reasons? For instance, I’ve used time series analysis techniques to estimate the strength of selection in algal communities (Fox et al. 2010), thereby directly testing whether algal communities are neutral or not (they’re not). In this context, this is a much more direct and powerful approach than trying to distinguish neutral and non-neutral models by testing their predictions (e.g., Walker and Cyr 2007 Oikos) (UPDATEx2: The example of Fox et al. 2010 isn’t the greatest example here, because while it is an assumption-testing study, it’s not actually an experiment. Probably should’ve stuck with Wootton and Pfister’s first example of testing evolution by natural selection by conducting experiments to test for heritable variation in fitness-affecting traits, which are the conditions or assumptions required for evolution by natural selection to occur. And as pointed out in the comments, the Walker and Cyr example isn’t great either because they actually were able to reject the neutral model for many of the species-abundance distributions they checked, in contrast to many similar studies)....