The Real Meaning of Slip Probability

Every slip starts with a mismatch — between how much grip a person needs to walk and how much grip the floor actually provides. Measuring that mismatch is at the heart of tribology, the science of friction, and tribometer friction testing, the method used to quantify it.

But here’s the key: a friction number means nothing without knowing which tribometer produced it, under what condition set, and how it was calibrated against real slip events. As Carl Strautins, one of the world’s leading experts in tribology, puts it: “A coefficient of friction number without specifying the model or device that produced it is utterly useless.”

To understand how modern slip probability models developed — and why different tribometers produce different results — we need to look at the four key frameworks that shaped the field: the Pye model, the PSRT model, the VIT model, and the Pendulum model.

The Pye Model: Friction Demand, Not Slip Probability

The Pye model isn’t a slip model — it’s a model of how much grip people need to walk. It was derived from ground reaction force data, the horizontal and vertical forces produced during gait. While valuable biomechanically, it isn’t part of the direct measurement chain for slip events. It assumes idealized walking that doesn’t hold in real-world environments.

That assumption became the foundation for many safety standards worldwide — including early standards like the ASTM C1028 and other Static Coefficient of Friction standards. Pye’s model predicts required friction, not the probability of slipping. Modern tribometry now treats that as an outdated baseline, useful for understanding human demand but not for quantifying actual slip risk.

The PSRT: The Breakthrough That Changed Everything

In 1999, Hanson’s PSRT study became the first to directly link tribometer readings to the probability of a slip — the first empirical connection between a test number and a human outcome. This was a milestone in applying set theory (as in George Cantor’s conceptual framework) to tribology. Each test condition represents a defined “set”: a particular speed, surface, shoe, and contaminant. The resulting probability applies only within that set — not as a universal truth.

In that defined condition — a person walking at a normal pace, unaware of a thin layer of water contamination on a flat surface — the PSRT provided measurable, repeatable slip probabilities. This was the birth of outcome-calibrated slip science.

The VIT: Same Framework, New Coefficients

The Vertical Incidence Tribometer (VIT), introduced by Burnfield & Powers in 2006, used the same mathematical foundation as Hanson’s PSRT — but different coefficients. In essence, each tribometer has its own equation, y = mx + b, where the slope (m) and intercept (b) change depending on how it interacts with the surface.

This proved something critical: any tribometer can predict slip probability, but only if it’s validated through human testing under known conditions. The VIT confirmed that calibration, not brand or mechanism, determines scientific legitimacy.

The Pendulum: A Validated and Reliable Instrument

The pendulum tribometer is one of several methods that have undergone full validation through human gait trials. Its reliability has been confirmed in the USC gait studies (2007–2024), where its measurements closely correlated with actual slip outcomes under standardized test conditions.

In modern measurement science, using multiple validated techniques helps reduce uncertainty and improve confidence in results. The pendulum serves as a benchmark for this level of scrutiny — not as the only acceptable method, but as a proven reference system that demonstrates what proper validation should look like.

Condition Sets Define the Meaning of “Probability”

When we say a surface has a “50:50 probability of slipping,” that probability isn’t universal — it applies only within a defined condition set. In this case: normal outdoor walking pace, water as the contaminant, a flat horizontal surface, and a standard rubber shoe sole.

This distinction matters. It’s not a “worst-case scenario” — it’s a controlled, repeatable reference point. Change the shoe, surface angle, lighting, or the person’s awareness of the hazard, and the probability curve shifts. That’s why results are always interpreted within the same standard and test condition set that produced them.

Perception, Footwear, and Human Adjustment

Strautins emphasizes that human perception plays a measurable role. People adjust stride and speed when they see or sense a hazard — reducing their required friction and, therefore, their risk. Tribometers model the floor, not human behavior, so perception remains a critical modifier in real-world slip probability.

Footwear compounds that variability. Flip-flops, high heels, and hard rubber soles each have different friction demands. When a person knowingly wears less stable footwear, they voluntarily increase their exposure to slip risk — an important consideration in forensic and legal analysis.

Why Context Matters More Than the Number

The industry still clings to “magic numbers” — 0.40 CoF, 36 PTV — without context. Strautins’ position is clear: a friction value alone is meaningless. Without knowing the instrument, calibration model, and condition set, you can’t interpret what the number represents. A pendulum 0.40 does not equal a PSRT 0.40 or a VIT 0.40 — each belongs to its own validated system.

For property owners and safety managers, this means that how a test was performed is just as important as what the number says. True slip safety depends on validated testing, documented calibration, and condition-defined probability models — not generic readings.