If the results do not support the hypothesis, the results should be used to

• A. Develop a new hypothesis which can then be verified by a newly designed experiment.
• B. Publish the results and declare the initial hypothesis invalid without further testing.
• C. Ignore the results and adjust the data to fit the hypothesis.
• D. Increase the sample size of the original experiment without changing the hypothesis.

Answer: (A)

When results don’t match what a hypothesis predicted, the next move is to adjust your thinking and test a new, more accurate explanation. This is how scientific reasoning advances: predictions must be tested, and if data contradict them, you form a revised, testable hypothesis and design a new experiment to evaluate it. The goal is to find an explanation that aligns with what is observed and to test that explanation under controlled conditions so the conclusions are well-supported.

Publishing results and calling the initial idea invalid without further testing skips essential validation and context. Ignoring the data or fabricating fit is improper and undermines science. Merely increasing the sample size of the same experiment without changing the hypothesis doesn’t address why the data disagree with the prediction; it may improve precision, but it won’t resolve the mismatch between the hypothesis and observations.