Which statement about changing only one factor is supported by the material?

Which statement about changing only one factor is supported by the material?

• A. It guarantees new discoveries.
• B. It helps identify gaps or errors in previous findings.
• C. It makes research faster but less robust.
• D. It is unnecessary if you have fresh data.

Answer: (B)

Isolating a single variable lets you attribute observed effects to that variable, which makes it possible to check the validity and completeness of previous findings. When you change only one factor and keep everything else the same, any change in the outcome can be traced to that factor alone. If the new results diverge from what earlier studies suggested, it points to gaps or possible errors in those findings, prompting a closer look at assumptions, methods, or conditions used before. This approach also helps test the reproducibility and robustness of conclusions by showing whether prior results hold up under a controlled, isolated test. The other statements aren’t supported because simply changing one factor does not guarantee new discoveries, and it doesn’t inherently make research faster at the cost of robustness. Even with fresh data, isolating a single variable remains a sound way to probe cause-and-effect and reveal any previously unnoticed limitations.

Isolating a single variable lets you attribute observed effects to that variable, which makes it possible to check the validity and completeness of previous findings. When you change only one factor and keep everything else the same, any change in the outcome can be traced to that factor alone. If the new results diverge from what earlier studies suggested, it points to gaps or possible errors in those findings, prompting a closer look at assumptions, methods, or conditions used before. This approach also helps test the reproducibility and robustness of conclusions by showing whether prior results hold up under a controlled, isolated test.

The other statements aren’t supported because simply changing one factor does not guarantee new discoveries, and it doesn’t inherently make research faster at the cost of robustness. Even with fresh data, isolating a single variable remains a sound way to probe cause-and-effect and reveal any previously unnoticed limitations.