What is one important guideline to follow when collecting data in an experiment?

Varying only one factor at a time when studying how a system responds is crucial in experiments because it allows for a clear understanding of cause and effect. This approach, known as controlled experimentation, helps isolate the impact of that specific variable on the outcome of the experiment. By keeping other factors constant, researchers can confidently attribute any observed changes in the results to the manipulated variable. This method enhances the reliability and validity of the experimental data, making it easier to draw meaningful conclusions about the relationships within the system being studied.

In contrast, taking all measurements at the same time can lead to confounding variables that may obscure the effects of the factor being studied. Relying on measurements that align with a hypothesis can introduce bias and compromise the integrity of the data collection process. Lastly, while it can be useful to analyze the relationship between variables graphically, insisting that measurements must form a straight line limits the types of relationships that can be examined and may not reflect the true nature of the data.