Statistical tests often uncover trends, but rarely give a clear-cut answer, with other factors often affecting the outcome and influencing the results.
Scientists then use a large battery of deductive methods to arrive at a hypothesis that is testable, falsifiable and realistic.
The precursor to a hypothesis is a research problem, usually framed as a question. For example, we might wonder why the stocks of cod in the North Atlantic are declining.
A research hypothesis, which stands the test of time, eventually becomes a theory, such as Einstein’s General Relativity.
Even then, as with Newton’s Laws, they can still be falsified or adapted.
Some examples could be: These are acceptable statements and they all give the researcher a focus for constructing a research experiment.
The last example formalizes things and uses an ‘If’ statement, measuring the effect that manipulating one variable has upon another.In fact, a hypothesis is never proved, and it is better practice to use the terms ‘supported’ or ‘verified’.This means that the research showed that the evidence supported the hypothesis and further research is built upon that.It can quite difficult to isolate a testable hypothesis after all of the research and study.The best way is to adopt a three-step hypothesis; this will help you to narrow things down, and is the most foolproof guide to how to write a hypothesis.If the researcher does not have a multi-million dollar budget then there is no point in generating complicated hypotheses.A hypothesis must be verifiable by statistical and analytical means, to allow a verification or falsification.The problem question might be ‘Why are the numbers of Cod in the North Atlantic declining?’This is too broad as a statement and is not testable by any reasonable scientific means.Scientists must generate a realistic and testable hypothesis around which they can build the experiment.This might be a question, a statement or an ‘If/Or’ statement.