History of soil geography in the context of scale

Miller, B.A. and R.J. Schaetzl. 2015. History of soil geography in the context of scale. Geoderma 264:284-300. doi: 10.1016/j.geoderma.2015.08.041. Continue reading “History of soil geography in the context of scale”

The Cycle of Science

In an earlier post I contrasted induction and deduction while suggesting that induction is the currently favored term used in science. However, I also suggested that the two philosophies can be used in concert with one another. Indeed, as much as one can argue about the virtues of one philosophy or the other, science actually advances on a cycle between the two!

Let’s review. To put things simply, induction emphasizes the individual, whereas deduction begins with generalizations. Science largely began with a focus on deduction, trying to find universal laws and hoping to explain as much about the world as possible. In the 19th century, generalizations reached a pinnacle of overuse, particularly for explaining human characteristics. While deduction became disfavored because of those missteps, induction rose to favor on the heels of more quantitative techniques for describing the individual objects of study.

Criticisms and attempts to further distinguish the two range from the practical limitations of the respective philosophies to the innateness of ideas. The debate over the origin of ideas about universals is as at least as old as Plato and Aristotle. For the most part, science has sided with Aristotle on the importance of experience (empiricism). In a way this explains science’s gravitation towards the principles of induction, but in the process, the problem of induction has been cast aside.

SextusThe problem of induction is the impossibility to observe every individual case. Here’s a classical quote on the issue:

When they propose to establish the universal from the particulars by means of induction, they will affect this by a review of either all or some of the particulars. But if they review some, the induction will be insecure, since some of the particulars omitted in the induction may contravene the universal; while if they are to review all, they will be toiling at the impossible, since the particulars are infinite and indefinite.

-Sextus Empiricus (160-210 CE) from Outlines of Pyrrhonism

In other words, there is always uncertainty about what hasn’t been directly observed and it is impossible to observe everything. To help fill the gaps between observations, we make generalizations that are based on what has been observed so far. In modern science these predictive guides take the form of models. There are different types of models, but one of the most popular is the empirical model. Defining a model as empirical emphasizes the fact that it is built from, or calibrated on, observed data. The purpose of the model is to then help the researcher understand system dynamics and make predictions. But the application of the model (generalization) to unknown situations is deduction.

cycle of scienceAlthough the terms regularly used today are different, in practice modern science uses induction and deduction in a cycle. Data is collected, which is often used to build models. Where weaknesses in these models are found, more data is collected and different model designs are tested in order to make a better model. Thus the cycle of science is a process of continually refining knowledge. Deduction enables predictions and helps to identify exceptions to the generalized models. Induction accumulates more data to test and improve those generalized models.


For more on the philosophical controversy of induction, deduction, and all the entities in between, check out the series of posts on The Lycaeum blog.

Also, hopefully it is easy to see how the scientific method fits nicely into this philosophical model. For example, tomatosphere.org presents the scientific method as a cyclical flow chart that parallels the diagram for the cycle of science presented here.

Is It ‘Deduction’ or ‘Induction’, My Dear Watson?

Sherlock Holmes often talks about ‘deductive reasoning’, but was he really using deduction or induction. Although by definition these two approaches appear to be opposites, in practice, the differences between the two can be subtle.

A simplified contrast between deductive and inductive reasoning is that deduction is reasoning from the top down and induction is reasoning from the bottom up. However, the modern definitions of these philosophies have many nuances, which address issues with both of these over-simplified descriptions and blur the lines between the two. Nonetheless, as a natural scientist, I view deduction as the formation of generalized rules that help prediction. Some have argued that deduction does not allow for a conclusion to be false, but that would not be science. It has been my observation that scientists using deductive reasoning make use of exceptions when they are discovered to refine understanding. For example, in this scene, Sherlock over-extends a generalization and learns a lesson that he probably won’t forget:

Inductive reasoning seems less comfortable with prediction, but provides better specifics about what is known and what is unknown. In other words, deduction has a problem with ‘not knowing what you don’t know’ whereas induction is more cautious by stating ‘what is known and how well it is known.’ Clearly these two philosophies have their respective strengths and can be used in concert with one another. However, it is interesting how deduction has come in and out of favor over time.

During the Age of Enlightenment (18-19th centuries), deduction was a popular mode of science. Because Sherlock (as well as the books’ author, Sir Arthur Conan Doyle) lived during the later part of this time, it makes sense that this would be the term chosen. Intriguingly, sometime between the 19th century and today, there has been a shift in scientific emphasis from the deductive to inductive approach. I suspect two potential causes for this.

First, in the 19th and early 20th centuries, science got into some hot water by making generalizations about humans. At the time, this wasn’t considered offensive and it was used improperly to support racist philosophies that were prevalent. However, today we do not tolerate racism (rightfully so!), and anything that was associated with racism has fallen out of favor (see environmental determinism).

The second reason for the shift, I believe, is due to the momentum of the ‘quantitative revolution’ in science. This transition in science is less of a revolution than it is often heralded to be. It also took various forms and happened at diverse times in different disciplines. In any case, it was a paradigm shift that put greater emphasis on the quantitative measurement of data and firmly placed a line between ideas that were supported by the data and those that were mostly speculative. Because speculative “arm waving” was fairly common in scientific literature prior to the ‘quantitative revolution’, I believe deduction suffered from its association. In addition, even though deductive approaches did regularly rely on quantitative measurements, the ‘quantitative revolution’ increased the focus on the data (fact collection). I think this is a false shift (deduction can be just as quantitative as induction), but the spirit of this ‘revolution’ brought induction into favor.

sherlock-holmes-147255_640So, was Sherlock using deduction? Yes, but his methods would be questionably scientific by today’s standards. Not because of his strategy, but because the generalizations he used to make his conclusions were based on experience (perhaps we could call this anecdotal evidence), as opposed to fully tested experiments using quantitative data to prove (or disprove) the generalization. Although in all fairness, Sherlock did say, “It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts” (from A Scandal in Bohemia) as well as “Data! Data! Data! I can’t make bricks without clay” (from the Adventure of the Copper Beeches).

Here is how Sherlock described his process, “In solving a problem of this sort, the grand thing is to be able to reason backwards. That is a very useful accomplishment, and a very easy one, but people do not practice it much. In the every-day affairs of life it is more useful to reason forwards, and so the other comes to be neglected…Let me see if I can make it clearer. Most people, if you describe a train of events to them, will tell you what the result will be. They can put those events together in their minds, and argue from them that something will come to pass. There are few people, however, who, if you told them the result, would be able to evolve from their own inner consciousness what the steps were which led up to that result. This power is what I mean when I talk of reasoning backwards, or analytically.” (from A Study in Scarlet)

More quotes from Sherlock Holmes on this topic

Is It a Scientific Theory or Hypothesis?

This is a common question addressing a popular misconception about how science classifies the knowledge that it has accumulated. The levels of hypothesis to theory to law often get interpreted as classes of confidence. However, this is not really right. The missing piece here is spatial scale!

It isn’t easy to draw the line for what is scientifically known and what is not. A major reason this line is so blurry is that knowledge can be applicable at different spatial scales. The three categories of scientific understanding (hypothesis, theory, and law) are often defined in introductory science courses as describing how thoroughly a concept has been tested. Although theories do need more testing than hypothesis and laws more testing than theories, the amount of testing required is only a consequence of the real definitions. These three classes are actually describing the extent over which we know an idea to be true. The larger the extent, the more testing that will be required to know that the concept is applicable across the entire extent.

science scalesBecause we encounter phenomena at a very local scale, this is usually the beginning of our ideas about how the world works. Based on these few observations, or anecdotes, we form a hypothesis about what is happening and why. In popular culture, this idea might be called a ‘theory’, but science would require proof that the concept applies in more situations before elevating it to a theory.

As we gather more data and test the idea more, we can start to explain connections between several phenomena. When we have evidence that a concept has greater applicability than to just a few anecdotes, then we can upgrade it to a theory. Although more testing was required to make this change in knowledge classification, the important part is that we now have confidence in a wider applicability of the idea.

At the top of this hierarchy of scales are scientific laws. These are concepts that have proven to be universally true. Clearly, to be confident that something is universal requires a lot of testing, which is why there is sometimes confusion about the meaning of these categories of scientific knowledge. Being universally applicable does not mean that laws cannot have defined conditions under which they apply. In fact, many are limited to certain conditions, but even with those conditions, scientific laws are expected to be reliable no matter where you go in space.

The clarifications I provide here do not contradict popular definitions, but they point out that oversimplified definitions focus on the wrong part of the scientific knowledge classification, which leads to misconceptions. At a single site, one can make millions of observations of the same thing happening the same way. However, the tested explanation for this one location will never be considered a law, despite a high confidence in that explanation being able to predict the same event occurring again. This is key to understanding why some scientific explanations with a high level of confidence will never get promoted to a higher category of scientific understanding.

To give a specific example, we have observed on the planet Earth that the acceleration due to gravity is between 9.76 and 9.84 m/s2. Despite the high level of confidence in the truth and predictability of this fact, it alone could never be promoted to a law. It cannot be considered a law because it lacks universal applicability. Observations of this phenomenon (plus celestial bodies) did lead to Newton’s Law of Universal Gravitation, which identifies a gravitational force between any two masses that is equal in magnitude for each mass, and is aligned to draw the two masses toward each other:

[latex]F = G \left ( \frac{m_1m_2}{r^{2}} \right )[/latex]
where, m1 and m2 are the two masses, G is the gravitational constant, and r is the distance between the two masses.

This equation is capable of being a scientific law because it has been generalized to be universally applicable. Of course, rates of gravitational acceleration on Earth are still useful and reliable. The same can be said for many other pieces of scientific information that cannot be or have not yet been established for greater extents.

For more, check out: Berkeley’s webpage on “Science at multiple levels”

The Scientific Method: Does Anybody Really Use It?

It can be hard to match what we learned about the scientific method in school with the scientific literature being published. There are a variety of reasons for this, but generally it is an issue of what is practical (especially within the time frames that sources of funding expect results).

Because the classic description of the scientific method is a bit idealized, I’ll attempt to summarize the process in a way closer to reality:

    1. Identify opportunities to expand knowledge. A scientist puts together an idea based on what is already known and creative ideas on how to make improvements (observation and maybe hypothesizing). The new research could help fill gaps in knowledge or attempt to improve methods. These new questions are often guided by the problems that society needs to solve.
    2. Design a systematic means to collect and analyze data that will accomplish defined goals. The scientist designs a research plan that will expand knowledge, but because modern science is often chipping away at complicated issues, there may not be a formal hypothesis tested.

      If a hypothesis is not tested, is the research still scientific? Yes. Maybe we should call this activity scientific exploration in contrast to scientific experimentation, but both activities are needed to expand the body of scientific knowledge. The key is how the data is collected and analyzed. In both cases, reliable (precise) measurements must be taken.

    3. Do the research (experiment and assess data). In real life, things rarely go as planned. To keep the research scientific, one needs to keep track of the surprises that come up and any adaptations that were done to the work in response to them.
    4. Document. This is a critical and sometimes tricky step. Nobody has time to read a detailed log on everything that happened throughout the research project. The work has to be synthesized, but in a way that is transparent enough for others to evaluate and potentially build on the work.

In the practical world of doing scientific work, it is often necessary to find projects that make incremental progress towards a bigger scientific question. Therefore, useful projects could be primarily about the collection of data or simply the improvement of a method. In this context, a critical challenge to scientific work is making valid comparisons between studies. This need for comparisons makes the use of standardized measurements and analysis methods very important.

Although, the scientific method may not be clearly visible in modern research studies, the logic principles behind it are still essential to scientific research. The causes of phenomenon (whether it be a bug in a software program or the distribution of soil properties around the world) are determined by a systematic process to eliminate alternative causes. Only when all of the alternative possibilities are eliminated can conclusions be drawn. When single studies cannot complete this task alone, they advance science more cautiously by only proposing what the results suggest and being careful to not extend conclusions beyond what is supported by available data.

What is Science?

For my first blog post, it makes sense to start with the basics. And one of the most fundamental concepts for a researcher to establish is the definition of science. As a starting point, let’s begin with the definition from the Oxford dictionary (which is very similar to definitions found elsewhere):

def. Science – the intellectual and practical activity encompassing the systematic study of the structure and behavior of the physical and natural world through observation and experiment.

I think the key phrase in this definition is that science is a “systematic study,” which establishes the epistemology of science. The critical approach for evaluating information and ideas is what separates scientific knowledge from all the other ideas that are out there. The important thing to understand is that science is very deliberate in defining what is known and what is unknown. The fact that scientific knowledge has a high standard for establishing something as ‘known’ is what makes it special. The drive to move more things from the unknown category to the known category is what makes science exhilarating.

Part of the special standard of knowledge for science is the required consideration of the possibility that an idea can be disproved. If an idea is not testable (falsifiable), then it cannot meet the standards of scientific knowledge. Similarly, science is always seeking to improve itself by never assuming that new information couldn’t provide reason for revising or even throwing out old ideas that seemed well proven in the past.

After saying all of this, I should make mention of the practical work of science. Expanding knowledge is a general goal of science, but to serve other societal needs, we often focus the use of this knowledge for prediction. In order for us as people to decide on the best actions to take, we want to know what the impacts of those decisions will be. Therefore, a lot of scientific work focuses on finding better ways to do things and identifying hazards. Of course, prediction is a risky business, but science provides us with a means for increasing our confidence in those predictions.

The philosophy of science is remarkably still heavily debated, but maybe that makes sense, because one of science’s strengths is that nothing should be left unquestioned. However, there are several central principles that modern scientists adhere to. For a more in-depth look at this topic, check out: A Guide to Understanding Science 101