A hypothesis refers to a provisional idea whose merit requires evaluation. Hypothesis is a testable statement, true or false, of a specific relationship between or among variables. Teachers may simplify the meaning of the term "hypothesis" by using the phrase "an educated guess" instead of hypothesis. In the classic model of science hypothesis or testable statement is deduced from a theory. We generate hypotheses as early attempts to explain patterns observed in nature or to predict the outcomes of experiments. The framer of a hypothesis needs to define specifics in operational terms.
A hypothesis requires researcher to either confirm or disprove it. A confirmed hypothesis may become part of a theory and may become a theory itself. Karl Popper's hypothetico-deductive model of science requires falsifiable hypotheses. A hypothesis cannot be "confirmed", because there is always the possibility that a future experiment will show that it is false. Hence, failing to falsify (falsifiability or refutability) a hypothesis does not prove that hypothesis: it remains provisional. However, a hypothesis that has been rigorously tested and not falsified can form a reasonable basis for action, that is, we can act as if it is true, until such time as it is falsified.
According to Schick and Vaughn, researchers weighing up alternative hypotheses may take into consideration Testability, Simplicity, Scope, Fruitfulness and Conservatism. The hypothesis contrary to the null hypothesis, usually that the observations are the result of a real effect, is known as the alternative hypothesis.
A null hypothesis, a concept introduced by R. A. Fisher, is a statistical hypothesis that is tested for possible rejection under the assumption that it is true (usually that observations are the result of chance).
What is a Hypothesis in Machine Learning? by Jason Brownlee.
The discussion of hypotheses in machine learning can be confusing for a beginner, especially when “hypothesis” has a distinct, but related meaning in statistics and more broadly in science. In this post, you will discover the difference between a hypothesis in science, in statistics, and in machine learning.
After reading this post, you will know:
A scientific hypothesis is a provisional explanation for observations that is falsifiable.
A statistical hypothesis is an explanation about the relationship between data populations that is interpreted probabilistically.
A machine learning hypothesis is a candidate model that approximates a target function for mapping inputs to outputs.