COMPARISON
Size recommendation: Statistics vs. Physics
Not all size recommendations work the same. The difference lies in the model — and in the result for your customers.
Two fundamentally different approaches
Statistical approach
Data source
Purchase and return data from other users
Method
Correlation and pattern recognition
Accuracy
1.5–2 cm typical deviation
Result
"Size M recommended" — a number, no explanation
Garment analysis
Based on generic size chart
Cold start
Needs thousands of data points per product
Examples
True Fit, Fit Analytics, quiz-based tools
Physics-based approach (maketribe)
Data source
Real body measurements + real garment measurements
Method
Mathematical model / deterministic simulation
Accuracy
0.59cm deviation
Result
Per-body-area fit analysis with size comparison
Garment analysis
Actual dimensions, cut, material, elasticity
Cold start
Works from the first product without historical data
Examples
maketribe
Why the approach matters
Size ≠ Fit
A size M says nothing about how a garment fits at the shoulders, chest, waist, or hips. maketribe analyzes fit per body area.
Data ≠ Understanding
More data doesn't mean better recommendations if the underlying model doesn't understand clothing. maketribe models cut, material, and elasticity.
Correlation ≠ Causation
Statistical models recognize patterns but don't understand why something fits. maketribe's physics-based model calculates actual fit.
Comparing different size recommendation approaches reveals: The quality of a recommendation depends not on the amount of data, but on the model. While statistical systems guess from noisy purchase data, maketribe calculates fit based on real body and garment measurements — with 0.59cm accuracy and per-body-area analysis.