Asked by kj
Sir William Bragg, a physicist who used x-rays to examine crystal structures, stated; "The most important thing in science is not so much to obtain new facts as to discover new ways of thinking about them." When analyzing data, a researcher may find that the evidence does not support the original hypothesis. What should the researcher do?
Change the original hypothesis so that the evidence supports the data.
Change the data to ensure that the evidence supports the original hypothesis.
Use the data to generate further questions and possible new experiments and explanations.
Use the data, excluding the first trial, and repeat the experiment until the data collected is usable.
Change the original hypothesis so that the evidence supports the data.
Change the data to ensure that the evidence supports the original hypothesis.
Use the data to generate further questions and possible new experiments and explanations.
Use the data, excluding the first trial, and repeat the experiment until the data collected is usable.
Answers
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Answered by
GPT-5 mini
Correct answer: Use the data to generate further questions and possible new experiments and explanations.
Why: Good science follows the evidence. If results don’t support the original hypothesis, you should
- verify the data (check for measurement/recording errors, equipment faults),
- repeat the experiment as needed (but report all trials and why any were excluded),
- consider alternative explanations and refine or replace the hypothesis based on the evidence,
- design new experiments to test the revised ideas,
- report the findings honestly (including negative or unexpected results).
What not to do: deliberately alter data or hide trials (fraud/cherry‑picking) or retroactively force a hypothesis to match the data without justification. Revising a hypothesis is appropriate only when done transparently and based on evidence.
Why: Good science follows the evidence. If results don’t support the original hypothesis, you should
- verify the data (check for measurement/recording errors, equipment faults),
- repeat the experiment as needed (but report all trials and why any were excluded),
- consider alternative explanations and refine or replace the hypothesis based on the evidence,
- design new experiments to test the revised ideas,
- report the findings honestly (including negative or unexpected results).
What not to do: deliberately alter data or hide trials (fraud/cherry‑picking) or retroactively force a hypothesis to match the data without justification. Revising a hypothesis is appropriate only when done transparently and based on evidence.
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