I Could Be Wrong

I could be wrong.

This is something that every true scientist must face directly. Our mental constructs to explain the real world have assisted us greatly over many millennia. Today more than ever, we need to remind ourselves that our ideas can be mistaken. Scientists have a particularly difficult time with this, as they must become their own worst critic. Not everyone can do this.

What I do is to set my work aside for an extended period of time (dear reader, please forgive my blogging mistakes). Any period of time, from a day to years, helps to clarify issues that are initially unrecognized. This rest allows for the injection of new knowledge and a more objective perspective to grasp the larger picture.

Scientists have the additional burden of mathematics. Mathematics is a two-edged sword that can slice the Gordon's knot or cut off our hand. True science needs mathematics to objectively describe its findings. The problem with mathematics arises because there may exist more than one mathematical solution to a real world physical problem. Scientists have recognized this and decided that the simplest way to describe the real world problem is the best. This has been termed Occam's razor (sometimes spelled as "Ockham's razor" attributed to the 14th-century English logician and Franciscan friar William of Ockham).

If a scientist finds a mathematical solution that isn't so simple, then they often have a dilemma. The dilemma often occurs if they try to fit their data to some mathematical equation that fits the data, but only if they use higher and higher exponential powers of a particular parameter within their equation. The problem is that this process begins to lose the physical meaning behind that parameter and the logical connection to the underlying physical process becomes less clear.

Those who aren't scientists don't understand this dilemma and fail to recognize some of the serious implications for today's scientists. The old saying is, "Publish or perish"; however, my former mentor use to say, "Publish and perish!". This is a problem for both the experimental and theoretical scientists today because they have enormous pressures on them to publish their findings before they get scooped, or to get that grant, or to patent a promising technology, or to show a potential boss that they've published many papers. It is no wonder that the cold hard logic underlying the science often gets lost in these scenarios. It is also no wonder that if they have at least one mathematical equation that fits most of their data, then they will publish it without asking themselves what does it mean and what are the implications of using this equation. In other words the bigger picture often gets lost in the rush to publish and a scientist may lose their hand to the mathematical sword.

This leads one to consider the region of what I'll call "hard science". Hard science is the objective modeling of experimental findings using more than one model and then evaluating which model is best. Today this is almost impossible to do within one scientist's lifetime, because almost 90% of the scientists who've ever lived are alive today and they are exponentially adding to the scientific literature in their rush to publish so that there has been an exponential explosion in potential mathematical models in all scientific areas. No one scientist can test them all.

So what can hard science do? There are additional aspects within the mathematical equations to look for. First, the generality of the equation to other experimental findings is an important aspect. Second, the novelty of predictions arising from manipulations of the parameters within the equation and the experimental verification of these predictions. Third, the ability of the parameters within the equation to match with physical entities. This places the burden on the experimentalists to wisely choose the theory that they are testing. Thus are the scientific models that will lead us toward a better understanding of our universe proven true. This involves a lengthy process of give and take between theory and experiment to find the truth behind our scientific discoveries today.

I could be wrong.