What is the difference between a hypothesis and a theory in science?

In scientific methodology, a hypothesis plays a crucial role. It is essentially an educated guess, a prediction based on existing knowledge and prior observations. More precisely, a hypothesis is a testable statement about the relationship between two or more variables. For example, you might hypothesize that 'plants grow faster under blue light'. This is something you can test through experiments and observations. A good hypothesis should be clear and concise, giving you a direction for conducting your experiment. It helps to set the stage for scientific inquiry by giving researchers specific predictions to work with.

A theory is a well-founded explanation of an aspect of the natural world. Unlike a hypothesis, which is a single statement to be tested, a theory is a comprehensive framework. It is built on a substantial body of evidence that has been repeatedly confirmed through observation and experimentation. When we talk about scientific theories, we're talking about explanations that integrate and generalize multiple hypotheses that have consistently been supported by data. For example, the theory of evolution isn't just a single idea. It encompasses numerous hypotheses and facts about how species change over time, drawing evidence from genetics, fossil records, and other scientific disciplines.

The main difference between a scientific theory and a hypothesis is their level of acceptance and the breadth of evidence supporting them. A hypothesis is an initial, narrow, testable statement. It is something you start with during an experiment, aiming to either confirm or refute it through your findings. For instance, your experiment might start with the hypothesis that 'plants grow faster under blue light'.
On the other hand, a theory is far more comprehensive. It provides a broader explanatory framework that unifies different hypotheses and data. A theory has undergone extensive testing and is accepted by the scientific community as the best explanation based on current knowledge. For example, the theory of gravity explains a wide array of phenomena related to the force that objects exert on each other, backed by years of evidence and multiple experiments.

In science, evidence and experimentation are the cornerstone of validating both hypotheses and theories. The scientific method relies heavily on collecting empirical data through experiments to test hypotheses. When you generate a hypothesis, you must design experiments that control variables carefully to isolate the factor you're testing. If your hypothesis, like 'plants grow faster under blue light', is confirmed by multiple experiments, it adds to a body of evidence that might eventually contribute to a broader theory.
However, even well-supported hypotheses can be modified or rejected in light of new evidence. This is why reproducibility—being able to get the same results through repeated experiments—is so important. Over time, as experiments are replicated and data accumulates, scientists can build theories that provide a general framework for understanding natural phenomena. Theories are constantly tested and retested, and while they are robust, they are always open to revision or rejection if new evidence comes to light.