Every true experimental design must have this statement at the core of its structure, as the ultimate aim of any experiment.
The hypothesis is generated via a number of means, but is usually the result of a process of inductive reasoning where observations lead to the formation of a theory. 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 problem, usually framed as a question.
The precursor to a hypothesis is a research problem, usually framed as a question. It might ask what, or why, something is happening.
For example, to use a topical subject, we might wonder why the stocks of cod in the North Atlantic are declining. 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. It is merely a tentative question arising from literature reviews and intuition. Many people would think that instinct and intuition are unscientific, but many of the greatest scientific leaps were a result of ‘hunches’.
The research hypothesis is a paring down of the problem into something testable and falsifiable. In the aforementioned example, a researcher might speculate that the decline in the fish stocks is due to prolonged over fishing. 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. Some examples could be:
Is over-fishing causing a decline in the stocks of Cod in the North Atlantic?
Over-fishing affects the stocks of cod.
If over-fishing is causing a decline in the numbers of Cod, reducing the amount of trawlers will increase cod stocks.
These are all acceptable statements and they all give the researcher a focus for constructing a research experiment. Science tends to formalize things and use the ‘If’ statement, measuring the effect that manipulating one variable has upon another, but the other forms are perfectly acceptable. An ideal research hypothesis should contain a prediction, which is why the more formal ones are favored.
A hypothesis must be testable, but must also be falsifiable for its acceptance as true science.
A scientist who becomes fixated on proving a research hypothesis loses their impartiality and credibility. Statistical tests often uncover trends, but rarely give a clear-cut answer, with other factors often affecting the outcome and influencing the results.
Whilst gut instinct and logic tells us that fish stocks are affected by over fishing, it is not necessarily true and the researcher must consider that outcome. Perhaps environmental factors or pollution are causal effects influencing fish stocks.
A hypothesis must be testable, taking into account current knowledge and techniques, and be realistic. 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.
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.
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.