Science is good as well as bad. Justify
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The good, the bad and the ugly science: examples from the marine science arena:-
Department of Environmental Science and Policy, George Mason University, Fairfax, VA, USA
The terms “good science,” “bad science,” and especially “sound science” are frequently used in the policy arena. Most often, this is so parties with interests (usually economic) in the outcome of a political decision can promote certain results and attempt to discredit others. It has been argued that the terms “sound science” and “junk science” have been appropriated by various industries, such as the oil and gas industry and the tobacco industry. “Junk science” is the term used to tar scientific studies that disagree with positions favorable to the industry (Mooney, 2004, 2006; Oreskes and Conway, 2011; Macilwain, 2014). But can science actually be “good” or “bad”?
Science is a process. It's the act of taking observations made in the natural world to test hypotheses, preferably in a rigorous, repeatable way. The tested hypotheses are then rejected if they fall short, rather than accepted if the data are compatible, and the results are ultimately critically reviewed by the scientific community. Concepts that work survive, whereas those that do not fit the observed data die off. Eventually, concepts that survive the frequent and repeated application of enormous amounts of observational data become scientific theory. Such theories become as close to scientific fact as is possible—nothing can be proved absolutely. This process holds for social science as much as for chemistry, physics or biology: it does not matter if the data come from surveys or observational data from humans. A study either follows this protocol or it does not. Put simply, it is science or it isn't science.
That said, what is sometimes referred to as “bad science” is the use of a bad experimental design. This is typically a set-up that has not accounted for confounding variables, so the hypothesis has not been appropriately tested and the inferences based upon this work are flawed and incorrect. These flaws may include the use of an inappropriate sample size or time frame. Use of selective data is another problem, where data that don't fit are simply left out of statistical analyses as “outliers.” In short, “bad science” is a study that does not follow the scientific process. It could also be used to describe studies that have flaws and limitations that are not highlighted by researchers. The term “bad science” has also been applied to inappropriate interpretations of the results. The reason for this, mentioned above, is that science never proves anything. Thus personal opinions can color interpretations of what data actually mean. This is where most of the debate in the scientific community really lies. We may all agree that a given hypothesis has not yet been invalidated, but what if alternative explanations for the observed data are possible? Or, as noted above, there might be limitations and caveats in particular study—for example an ex situ experimental study on a small sample of a single species in an aquarium produces interesting results, but to ignore these limitations and extrapolate these results to make conclusions about multiple species in multiple ecosystems in the wild over-reaches the real limits of the study in question (see Parsons et al., 2008 for an example related to captive cetacean studies and the impacts underwater sound). However, when scientific studies are interpreted beyond hyperbole, and are deliberately misinterpreted to fit a particular world view or to favor special interests, this is when science is no longer just “bad,” but it becomes ugly.
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science is good as well as bad justify