Can't find your keys? Odds are a unicorn didn't eat them, you just forgot where you put them.
End up at the Clermont Lounge instead of the Clairmont Inn? You'll probably want to check your GPS instead of looking for a wormhole. (Or just enjoy the Clermont Lounge... But I digress.)
Occam's Razor is also incredibly important to developing models to explain biological data. Statisticians often speak of overfitting models (see blog post by Ashley Cross) as a common temptation and problem for scientists. Yes, it may be possible to construct an equation that perfectly explains each and every one of your data points. This strategy not only disregards the inherent variability within biology, it also makes it much more difficult to apply the model to other systems.
However, the apparent simplicity of making a simple model should also be taken with a grain of salt. Simplicity is easy and easy explanations are comforting. It is much more reassuring to believe that you mistyped into your GPS than to believe you fell into a worm hole. But imagine the experience you'd have disregarded if you actually did fall into a worm hole.
Models are derived from your sample which a) by chance, may not represent the population you hope to extrapolate the results to, or b) could be impacted by an enormous number of factors that you have no control over, or don't know exist. Good hypotheses are based off of educated predictions and previous knowledge, but absolutely nothing is completely understood. Biology is complex, and we often take complexity for granted. The people over at LessWrong give several examples of this. Most relevant to this discussion is the example of Thor, the angry god to which ancient people attributed lightning strikes:
"The human brain is the most complex artifact in the known universe. If anger seems simple, it's because we don't see all the neural circuitry that's implementing the emotion... The complexity of anger, and indeed the complexity of intelligence, was glossed over by the humans who hypothesized Thor the thunder-agent."
Though most of us probably don't pray to Thor during every rainstorm, we may still be equally likely to oversimplify as to overcomplicate. Models are incredibly useful and important. They save time and energy, but they are descriptions and not explanations. If you have read Part G of Intuitive Biostatistics, it is easy to see that choosing how to construct or compare models can be a tenuous process if not given enough though. It is always important to understand the problem you are trying to address, but we must be careful as scientists to understand we have limited understanding.
