Introduction
In Science every piece of knowledge is established through a rigorous methodology that seeks to keep balance with all other concepts already proven to be true. There are methods of proof that are used for both the experimental and the theoretical. It is well accepted that scientific methods change with various scientific disciplines and hence there is no single defined approach (Gendron, n.d.).
In a general scope however, developing new knowledge requires some level of suspicion based on our intuitive perception and acquired knowledge of everyday phenomena. An argument must be provided to support or disapprove these hypotheses and dispel any uncertainty that is laid upon them.
Therefore, the scientist does not in fact simply take sides in the proposition of a hypothesis. He merely seeks to find out the truth or falsehood of his claim at which point any conclusion reached is recognized as a universal valid establishment of scientific truth. With this in mind, we see that science develops itself from a synthesis of principles that relate to each other without contradiction.
Properties of the scientific method
The scientific method has developed over time to include various viewpoints from a host of scientists and philosophers. Aristotle emphasized on the use of empiricism in deducing rationale for the creation of scientific facts.
The ideas of Aristotle were carried over in the 12th and 13th Century by thinkers such as Grossteste and Roger Bacon to realize a new definitive methodology of testing new scientific phenomena (Stanford Encyclopedia of Philosophy, 2007). This method involved a cycle that transitioned between observation, hypothesis, experimentation and verification.
In the verification stage, one’s work must be reviewed by peers who have a suitable understanding of the subject to affirm the findings made. This process has been critical in the functioning of both applied and theoretical scientific fields of inquiry. For instance, in medicine the method is applied in every day practice when diagnosing and treating ailments.
The impact of uncertainty in natural sciences
Most of the hypotheses in natural science are substantiated with an acute reliance on pre-defined models. We often make a prediction which will then either be proved through experimentation or model testing. For example, in an average clinical institute, symptoms of a disease will normally be provided by a patient.
The medical practitioner attending to the patient will then use his knowledge of associated symptoms to diagnose the patient’s ailment. If experimental methods are used to diagnose symptoms, the final inferences made will still depend on existing models which link observed traits to known conditions.
The more refined this process is, the more successful it becomes in determining the cause. Possibilities of models that have not been discovered still exist and this is normally characterized in events that lead to the discovery of new diseases and pathogens. In essence, we see a process where scientific method is used to discover new problems in the course of resolving others. The complexity of such diagnosis methodologies can preempt proper treatment when a set of symptoms can be linked to a wider variety of ailments or syndromes.
In ecological research, hypotheses are often evaluated through statistical methods. Statistical methods make use of probabilistic and inductive reasoning methods to base their arguments. The numerical evidence to support a hypothesis is often confined to a set of queries which emanate from presumptions about the model that can be used to test the hypothesis (Hobbs & Hilborn, 2006).
Conclusion
It can thus be shown that to a large extent that the enormous set of possible variables that pertain to all phenomena in the natural sciences may inhibit proper applicability of the scientific method in developing a sound understanding of these topics. Uncertainty hence provides a useful device for continually eliminating the margins of error that exist in current scientific knowledge.
References
Gendron, R.P. (n.d.) Observation and Hypothesis Testing In Ecology. Web.
Hobbs, N.T. & Hilborn, R. (2006) ‘Alternatives to Statistical Hypothesis Testing In Ecology: A Guide to Self Teaching.’ Ecological Applications 16(1), 5-19. Web.
Stanford Encyclopedia of Philosophy. (2007). Roger Bacon. Web.