Objectivity and rationality are critical components for solving a natural science problem as it is for engineering and core sciences.
A quick overview of the two specific streams and how these skills are applied in them –
Rationalism and Objectivity in Sciences
The purpose of science is to trace within the chaos and flux of phenomena, a consistent structure with order and meaning. Conforming with reason lies at the core, otherwise called rationalism. Science is founded on the hope that the world is rational in all its observable aspects.
Science is not about truth, it is about testability. It provides concrete, verifiable results that are reproducible and determinative.
Science could be defined as a dialogue between mankind and nature. It does not, by itself, advocate courses of human action, but it can certainly throw light on the possible consequences of alternative courses.
Objectivity in science is an attempt to uncover truths about the natural world by eliminating personal bias, emotions, and false beliefs. It is linked to observation as a key element of the scientific method, testability, and reproducibility.
Rationalism and Objectivity in Natural/ Social Sciences
Natural Sciences build rational models based on empirical data. With reason as the chief source and test of knowledge as the underlying belief – “things are the way they are, for a reason” forms the basis of data collection, observation and deduction.
Objectivity is an important criterion during data ascertainment in natural science. Basically, it means that a method of data collection must always come to the same conclusion, regardless of who tests the empirical evidence.
Remember this; you are not a dreamer when you choose humanities. You’re swearing an oath to validate your big ideas using science to be a visionary.
Dream big and execute vs. Execute, and dream of scale
Traditional reasoning has boxed science as micro-level thinkers applying reason with keen attention to detail. And humanities as those with a macro-level approach to a problem.
While mostly agreeable, this may be a flawed categorization of streams and thinking.
Humanities/ Social sciences
The core tenet of this stream is exploring the social structure with a keen eye to understand the symptoms across multiple data points. More generalizations may have to be applied to arrive at a macro-level solution that produces an outcome. Although some of these are macro-level generalizations, a successful program requires the validation of biases and variables for success. This requires a micro-level analysis of implementations and success. A structure not just to create a program may be required but building a project exception within this structure has to be accommodated for the resounding success of an initiative in social sciences.
Sciences
Experimentally validating assumptions and variables for practicality is at the heart of science programs. This automatically ensures attention to detail. Attention to detail sometimes works as a fence against macro-level thinking. You could break this barrier by trying to accept what’s beyond the fences and look for scale from a scientific perspective, accepting unknowns and undefined variables that could be explored later. Try not to make perfection the enemy of success.
Thinking creatively within reason does sound a bit counterintuitive. But don’t be alarmed. Your creativity will not be curtailed but validated. For successful implementation of programs, this thinking maybe an essential ingredient.