Why clean data and correct equations can still mislead
Biomechanics often feels most convincing when the numbers look clean. Curves are smooth. Peaks occur where we expect them. Values are reported to several decimal places. The mathematics is correct, the software ran without error, and the results appear internally consistent.
This appearance of precision is reassuring.
It is also dangerous.
Many interpretive errors in biomechanics arise not from incorrect calculations, but from forgetting what those calculations actually represent. To understand why, it is necessary to examine the role of models.
Every Equation Is a Model
In biomechanics, nothing is measured directly in the form that we ultimately want to interpret. Joint moments, joint powers, muscle forces, and internal loads are not observed. They are inferred.
That inference occurs through models.
A model is a simplified representation of a system that allows us to make calculations that would otherwise be impossible. Segmenting the body into rigid links, assigning masses and inertial properties, estimating joint centres, and applying Newtonian mechanics are all modelling decisions. None of them are wrong. All of them are assumptions.
Even the most basic biomechanical equation rests on choices about:
- How the body is represented,
- Which forces are included or excluded,
- How anatomical structures are simplified.
The usefulness of a model lies not in its realism, but in its ability to clarify relationships in such a way as to reflect our current understanding of the world. The danger lies in forgetting that it is not the system itself.
Why Models Feel So Convincing
Biomechanical models produce outputs that look authoritative. This is partly because they obey physical laws and partly because computers are very good at arithmetic.
When a joint moment curve is smooth and repeatable across trials, it feels objective. When values are reported to three decimal places, it feels precise. When two studies show similar patterns, it feels explanatory. In fact, replication of studies is encouraged as this is a form of validity.
But none of these qualities guarantees that the model captures the underlying biological reality.
Precision describes the consistency of a calculation.
Accuracy describes how well it represents the system of interest.
Models can be precise without being accurate.
The Sources of Uncertainty We Rarely See
Much of the uncertainty in biomechanical analysis is hidden upstream of the final result.
Examples include:
- Estimates of segment mass and centre of mass location,
- Assumptions about joint centres, axes of rotation, and radii of gyration,
- Digital filtering choices applied to kinematic and force data,
- Simplifications about joint structures and muscle function.
Each of these decisions is defensible. None of them is neutral.
By the time a joint moment or power curve appears on a graph, layers of assumption have already shaped its form. The clarity of the output can obscure the uncertainty embedded in the process.
This is why biomechanical results should be interpreted as conditional statements: given these assumptions, this pattern is consistent with the observed movement.
Why More Detail Does Not Always Help
A common student instinct is to believe that adding more detail improves explanation: more segments, more muscles, more parameters, more decimal places.
Sometimes it does. Often it does not.
Increasing model complexity can:
- Amplify measurement noise,
- Introduce poorly constrained parameters,
- Ceate a false sense of confidence.
A simple model that is well understood can be more informative than a complex model that is poorly constrained. Competence in biomechanics includes knowing when additional detail clarifies interpretation and when it merely adds sophistication without insight.
The Illusion of Objectivity
Because biomechanical models are mathematical, they are often treated as objective. This is misleading.
Models encode choices. Those choices reflect:
- What the analyst believes matters,
- What can be measured reliably,
- What questions are being asked.
Two analysts can apply the same theoretical framework and arrive at different numerical results simply by making different, reasonable modelling decisions. This does not mean one is wrong. It means the results must be interpreted within context.
Understanding biomechanics requires accepting that objectivity does not mean independence from judgment.
What Competent Interpretation Looks Like
Competent biomechanical interpretation does not reject models. It respects them.
Strong interpretations:
- Acknowledge assumptions explicitly,
- Avoid over-specific causal claims,
- Focus on patterns and constraints rather than exact values,
- Remain open to alternative explanations.
Phrases such as “this result depends on the chosen model” or “within the limits of this analysis” are not disclaimers. They are markers of scientific maturity.
Why This Matters for Students
Students often lose marks not because their calculations are wrong, but because their conclusions imply more certainty than the model can support.
Examiners are rarely asking whether a number is correct in isolation. They are asking whether the student understands:
- What the number represents,
- How it was obtained,
- What it can and cannot justify.
Models are indispensable tools in biomechanics. But they are tools, not answers.
Where This Leads
If models impose structure on complexity, the next question is what happens when multiple structures can solve the same problem?
Human movement is not optimized around a single solution. It is adaptable, redundant, and variable. Understanding that variability is not a nuisance but a feature is essential to interpreting both experimental data and human performance.
In the next Foundations article, we examine redundancy, variability, and why the search for a single “correct” answer in biomechanics is often misplaced.