Final answer:
True, it is a best practice to align the relevancy of data with the hypothesis using appropriate observation tools. Data that does not support a hypothesis can still be informative for science. Justification for data selection is critical in ensuring data relevancy for the scientific question at hand.
Step-by-step explanation:
It is indeed a best practice to ensure the relevancy of data by using an observation or screening tool that is aligned with the hypothesis being tested. When formulating a hypothesis in science, researchers consider what they expect to observe if their assumption is correct. The data collected during the experimental phase must then be analyzed to determine if it corresponds with the expectations set forth by the hypothesis.
Data that does not support a hypothesis can still be useful as it contributes to the understanding of what does not hold true and can guide further investigations. Hence, not all data needs to directly support a hypothesis to be relevant or useful. Moreover, experimentation is not the only valid type of scientific investigation; observational studies and other methods can also yield valuable insights.
When selecting data types, it's critical to justify why certain data is necessary to answer specific scientific questions. This justification helps to ensure that the data being collected are directly aimed at elucidating the underlying mechanisms or phenomena being studied.