To quote the venerable Wikipedia:
Signal-to-noise ratio (often abbreviated SNR or S/N) is a measure used in science and engineering that compares the level of a desired signal to the level of background noise.
This has a number of applications in engineering, but it also nicely encapsulates a basic truth of dealing with equipment, people, and works – there is always a certain amount of background noise. (For more information on the concept, check out this article.)
In an ideal world, all equipment would be flawless and noiseless, everyone would be wonderful at what they do, and all works would be exceptional (don’t mind the inherent logical issue in the statement “all works would be exceptional”; I was trying to make a point, so please forgive me my linguistic foibles). As it so happens, due to a fun little thing called the Dunning-Krueger Effect, we tend to assume that our works are better than they actual are, until we reach a particular skill level, at which point we start underestimating our own skill levels. Succinctly put, we’re all terrible at figuring out how good or bad we are at any particular skill.
This is a real pickle, especially when dealing with other people. With very few exceptions, we’re going to end up working with other people. This is more applicable in cinematography than in photography, since there are usually more people involved with a film production than with a still photo shoot (although I’m sure there are plenty of instances which would prove me wrong in that aspect).
I’m going to restrict my examples to cinematography projects, to keep this a bit more manageable and readable.
First, look at your potential crew. If you’re dealing with a low-budget indie shoot, which most of us are, you are either not paying your crew or are paying them very little money. You’ve already eliminated the possibility of a great number of highly experienced, educated, and trained people being involved, simply by eliminating the carrot of a serious payday. It could be additionally argued that those who do work for a living are not necessarily great at what they do, but that’s a little tangential to the core of the argument. Out of those people, you’re generally going to tend to see the talent curve follow the infamous standard/normal distribution curve. This means some will be utterly atrocious, some will be genius, and the vast majority will lie somewhere between those two points. The outliers and bounds will depend on the sample size, but the general concept tends to be the same, which is that most people are not going to possess genius-level skills in the vast majority of a sample set.
Second, look at your potential actors/on-screen talent. You’re in the same boat as you were with the crew – except that a single truly terrible actor will destroy the illusion you’re trying to create, so you have to hope that your lower bound is pretty high, otherwise your end product will most likely suffer. (It should also be said that a really great director has been known to coax amazing performances out of lackluster talent, but we can’t all be Stanley Kubrick, Darren Aronofsky, or P.T. Anderson…)
So, what are we to do? We need to start acknowledging that not everything we’re going to make is going to be the best thing ever. It’s an anathema to the general self-aggrandizing L.A. film culture, but we have to respect the normal distribution curve – most films will be mediocre, some will be agonizingly bad, and some will be amazing. The DKE means we’ll always think that they’re all amazingly wonderful, but we need to be far more critical of our own works ; at least, if we want to try to produce high-quality output.
We also need to understand that there’s a place for our hubris, as well as our humility. We cannot grow as filmmakers, photographers, artists, or as people, until we both appreciate the skill-set we have, while at the same time understanding the limits of what we have accomplished right now, and always striving to produce better, become better, and encourage better. We can’t all be the best, but we can all become better.