Assumptions

According to the Oxford Dictionary the definition of “assumption” is:

Science and research are filled with assumptions.  Statistics and probability, filled with assumptions.  Models are built on assumptions.  In fact, models are oversimplifications of reality.  They are built from the assumptions of “experts” and are used to make predictions in the real world.  Science, probability, statistics, and modeling are not perfect because there is always error involved.  Measurement error, random error (sampling bias, animate vs inanimate, the environment of the assumption: experimental vs. observational) are all elements of potential error. 

“Great!  Who cares!  That’s the best we have!”  I’m fine with that response when chatting with fellow colleagues, however, my concern is understanding the assumptions involved.  In essence, improving critical thinking skills, and skepticism.  Don’t be so quick to state “the model proves…”, “the model shows,” “the model works,” without understanding the assumptions. We should strive to understand the underpinning presumptions regarding things such as: 

• Periodization Models

• Long-Term Athletic Development Models

• Training Residual Models

• Movement Models

• Biomechanical Models

“Science is the belief in the ignorance of experts.”  - Richard Feynman

Science evolves by creating better explanations for problems.  In order to do so, understand the assumptions behind the issue at hand. Question them, pick them apart, and strive to create better explanations.  At least, be able to defend them and understand their limitations.