Final answer:
In hypothesis testing, a prediction is made based on an initial hypothesis, and data is collected as evidence to support or refute it. If the collected data match the prediction, the hypothesis is supported; if not, it may lead to alternative hypotheses or further investigation.
Step-by-step explanation:
When conducting hypothesis testing based on research and available materials, a hypothesis is formulated as a testable statement to explain observations or experimental measurements. For example, the hypothesis might be "The number of deformed frogs in five ponds polluted with chemical X is higher than in five ponds without chemical X." A related prediction would be based on the assumption that if chemical X causes deformities, then we should expect to find more deformed frogs in the polluted ponds.
Once the hypothesis is in place, researchers will collect data to test it. They would look for evidence, such as counts of deformed frogs in the specified ponds, to either support or contradict the hypothesis. If Alexander Fleming were testing this hypothesis, he would gather relevant data as evidence through careful observation or experiment, comparing the frog populations. He would ask questions like, "If my hypothesis is true, what would I expect to observe?" and "Does what I observe match what I predicted?"
If there's a match between the prediction and the observation, the hypothesis is supported. If there's no match, the hypothesis is not supported, and this may lead to alternative hypotheses or further refinements of the original hypothesis. For example, if you find no difference in the number of deformed frogs between polluted and non-polluted ponds, you may need to adjust the hypothesis or consider other factors influencing frog health.