What Determines a Science
To determine whether a particular field of study, one which has vexed our imaginations with awe and curiosity, is a science or not requires us to attend, with great scrutiny, too but a few specific features.
The first and utmost essential feature of any science is whether it has at its core, like money in a bank account, something physical to reference; meaning, do the practitioners of the field study an object or fiction? If like a new-age guru, they claim knowledge about something intangible, then we shall worry very little, for it certainly lacks the essential quality of science: materialism. Science needs materialism in precisely the same manner in which pizza boxes need pizza; without a physical thing to speak of, there is nothing to consume.
The second feature in our list of criteria is quite the popper, like [K]arlton from the fresh prince. Terrible jokes aside, I am referring to the hotly debated notion of falsifiability. Though we shall explain in greater detail below what is meant by falsifiability, we need only know as of now that it is an essential feature for science.
The third little piggy that went to the market knew about the third feature of science, as he successfully brought back vegetarian bacon – whatever that is. The third feature, to no surprise, is prediction. If our field of interest can make correct predictions, then we have successfully stumbled upon a science; but if the field has yet to reach a correct prediction, then we are left holding nothing more than observations. Thus, we would, as of yet, not be dealing with a science.
The fourth feature for us to find, as though we were playing an intense game of where’s Waldo, is explanation. Now not all sciences necessarily need to have at the ready an explanation for their data, though most sciences do. So, explanation is something to consider, if even its consideration is not the utmost important.
When the majority of these features show up to our super-duper cool science party, featuring Pitbull screaming random Spanish lexical items over a mic, we will more than likely have a science.
Furthermore, it should be noted that each of these features can also be placed into categories of necessity and inductiveness; that is, some of these features are necessary, like observation, and others are rather less than necessary; these less than necessary features, like explanation, can, therefore, be used in the same way as an inductive premise, which is to say they inductively support a conclusion about whether a field of study is a science or not. For example, falsifiability, accurate prediction, and observation, when used together, are able to create a science, but explanation alone cannot formulate a science.
And lastly, before we go on to explain in further detail each previously mentioned feature, we ought to be clear about what we are doing; namely, we are drawing in the sand, like children, what the core features of a science are, which is not, “”how the central features of science interact with one another to create a science“. In a more simplistic way of saying it, we are concerned primarily with the ingredients, not the steps to cook the meal.
When we peer beyond the veil of our own ignorance to see the beauty that is reality, we begin to understand relationships amongst and features of objects or materials. In doing so, we immediately realize, as though we had just awakened from a slumber, all the richness that dances upon our senses; the physical world has impressed its knowledge upon our conscious experience.
As far as a scientist is concerned, we are perhaps in the utmost luck to have even a slight glimpse of the data-rich world. And the reason for the scientists’ boyish joy relates primarily to the fact that science takes those oh so wonderful patterns and features of the world to be their beloved objects of study. Science ponders the patterns and qualities the physical, so as to develop a greater understanding of the all-encompassing universe. The psychologist delights the desire for knowledge located by studying the nervous system in the context which it evolved to behave and perform within; whereas the physiologist, a lover of reduction, wishes to see the complexity of our wiring one piece at a time, and so he observes each part individually. In either case, the two scientists rely upon some form of observation, whether it be a contextually rich investigation or not.
Now, to further understand the nuance of the abovely put forth, it is certainly the case that we are quite capable of studying the material world without our doing so turning into a science, and that is what the study of philosophy would be. We can study ideas while still being something that is not as of yet a science.
The study of ideas has more to do with the logical entailment of concepts rather than the material or object, in the same way that software programming has more to do with the logic of certain languages rather than the hardware of the system. For example, I cannot determine whether a chair is good or bad through observation, I must instead consider the entailment of concepts and categories to do so. Consider the following argument, as it shall highlight further what is meant.
murder is evil
therefore, elves are evil.
The judgments we must make to determine whether that argument is strong or poor, which we shall from here on call validity judgments, relies on the entailment of concepts, and that is philosophy. It is to do less with the points of experience from which we derive observations and more to do with the thoughts which we associate with each meaningful observation derived from a point of experience. And so it would necessarily be about, indirectly, our observations, though it is directly dealing with the patterns of inference made. So, philosophy and science are different yet inextricably linked.
Whenever we make a knowledge claim and then test it with a prediction, we need to ensure that our prediction can be wrong. The reason being, if we cannot genuinely be wrong in our prediction, then we cannot determine the true accuracy of the knowledge. And might I add, I am not saying that we cannot have knowledge without falsifiability, for we certainly can; however, without knowing whether we were wrong or not, we don’t actually know if our knowledge is accurate. For example, suppose there were three red cups, placed upside down, on a table and we were faced with the challenge of choosing the one which contains a white ball; let us say we choose the most left cup, and we get a white ball. Now imagine this scenario repeats 10 more times, and each time you win. We might conclude that you’re a really good player, and the evidence would agree with such a claim. However, what if it were the case that every single cup contained a white ball? If that were the case, then your understanding of each victory changes drastically, only because you couldn’t have failed to begin with. That is what falsifiability does for us, it allows us to potentially fail, and so gives us a greater understanding of the data.
After we have performed our observation and have acquired a significant amount of data, we need to then progress onto prediction to test how well we know the data. And that is precisely what a prediction will allow us to do.
After the arduous process of collecting the data, we are ready to test our knowledge. So, let us suppose, for the sake of example, that we recently observed that 100 instances of a toaster toasting some bread, and from this, we believe that 2 minutes results in the best toast. From there, we can then predict that the bread will turn out better at 2 minutes than 1 minute at a much greater rate; meaning, out of 100 trials, we predict that the bread will better if toasted for 2 minutes rather than 1 minute. If our prediction is correct, then we know our belief is accurate. So, prediction allows us to determine the accuracy of our beliefs about observations.
Now, there was something quite important in the previous paragraph; namely, that our prediction about toast had conditions of satisfaction. Meaning, if our prediction was false and the data said 3 minutes was ideal, then our belief about 2 minutes being ideal would have failed to match the environmental conditions. We can call this the falsifiability principle.
Explanations adopt the role of semantics in scientific theories; that is, explanations fill the gap between data and prediction: namely, the question of why certain predictions accurately account for the data and other predictions don’t. Explanations add meaning to data.
To further understand the role of explanation in science, consider the following example: suppose every time your car took a left turn in your car, it made some random noise X. We could predict that pattern to occur at a constant rate. However, we would have failed so far to understand why the car makes noise X when performing a left turn. Such a mystery requires an explanation to be put forth, or else we will be unable to understand what that noise means with respect to the functioning of the motor vehicle. So, explanations give us the meaning of data.
Summary and Conclusion
To sum up, what we have said is that there are certain principles which must show up to the party if we are to have a science; that is, observation and prediction are absolutes in science. In addition to that, for us to even begin to know how accurate our knowledge about the world is, indeed, requires falsifiability and explanation; only because these two features, though not necessary, inform us about important features of our knowledge. So, to conclude, all sciences we currently have, I am certain, follow something incredibly similar to the abovely put forth outline.
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