Is Computational research valued more than Experimental research in Biological Science? Or vice versa?

Some researchers nowadays enforce computational research, while others believe that without experiments you cannot get the desired goal in Biological science.

Which factors do we have to consider before starting either computational or experimental research?

Are lab. resources serve as a reason for moving to computational research?

However, some researchers already discussed the pros and cons associated with each type.

Experimental research is a validation of theoretical outcomes, and computational biology is an opportunity to test hypotheses or analyze data with the help of a computer screen. Both types of research are important, and some studies might require experimental validation to justify their theoretical conclusions. For instance, in some fields such as chemistry, it may be necessary that theoretical work is supported by experiments. As much as computational research is crucial, validation experiments are needed in most cases.

Computational research

Computational research in biological science offers different advantages, i.e.,:

Mathematical modeling and simulation:
With the help of computational models that simulate how biological processes work one can understand what biological systems are. These models may provide insight into the underlying processes and inspire further investigation.
The use of computational analysis enables researchers to analyze and visualize large datasets identifying patterns that are obscure with other approaches.
Integration with other fields:
The field of computational biology brings together the domains of computation, life sciences and big data systems; it allows researchers to apply methods from different fields for solving biological problems.
Computational approaches can make replicating and opening results easier by encouraging researchers to share their code and methodologies more freely. Such computational research allows the investigator to test various hypotheses and scenarios, thereby offering remarkable freedom in studying a range of elements that might not be possible with experimental studies.

Experimental research

Experimental research in biological science has several advantages, including:

With experimental research, scientists can manipulate certain variables and determine if an outcome is feasible or not. In repeat experiments, the control of variables during replication ensures not only a validation but also improvement in results.
Validity and reliability:
In experimental research, it is possible to limit the variables’ introduction and minimize those which are unnecessary. In this case, experimental researchers can enhance the validity of the causal inference hypothesis and boost reliability by improving results.
Natural settings:
Scientific studies can be carried out under a controlled environment and deliver fast observations of the relationship between variables. In quasi-experimental designs, the real world is used to obtain a more reliable measure of how well an intervention or treatment works.
Scientific rigor:
The experiment is scientifically valid, thus hypotheses could be tested and new insights gained about systems in nature.

References:

https://vittana.org/16-advantages-and-disadvantages-of-experimental-research

https://www.broadinstitute.org/blog/whyiscienceqa-why-i-switched-experimental-computational-biology

https://doi.org/10.1038/s41592-021-01215-2

https://ecrcommunity.plos.org/2016/07/29/why-we-need-computational-models-in-biology/

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Is Computational research valued more than Experimental research in Biological Science? Or vice versa?