Navigating “tip fog”: Embracing uncertainty in tip measurements

Nature is full of messy variation, which serves as the raw material for evolution. Overlooking this variation not only weakens our analyses but also risks selecting inaccurate models, generating false precision in parameter estimates, and creating artificial patterns. Furthermore, the complexity of uncertainty extends beyond traditional “measurement error,” encompassing various sources of variance. To address this, we propose the term “tip fog” to describe the variance between the value from the overall modeled evolutionary process and what is recorded, without implying a specific mechanism. We show why accounting for tip fog remains critical by showing its impact on continuous comparative models and discrete comparative and diversification models. We rederive methods to estimate this variance and use simulations to assess its feasibility and importance in a comparative context. Our findings reveal that ignoring tip fog substantially affects the accuracy of rate estimates, with higher t..., , , ## Data from: Navigating “tip fog”: Embracing uncertainty in tip measurements

\"Tip fog\" is our term for what used to be called measurement error. Take a concrete example: squid tentacle length, for which we want to use the average value per species.

There are factors that lead to the given value (10.42 cm) for a species differing from the \"truth\"

* Sample variation: we measured only three squids in a species, not every individual.
* Measurement uncertainty: even if we measured every squid, tentacles are stretchy, so we can't know its true length to the micron.
* Pure error: someone measured in inches not centimeters for one of the three measured specimens
* Intraspecific variation: individuals differ in size

In past code, we've called this all \"measurement error\" (or its less melodious name, `mserr`) but they're distinct factors. One could call them \"noise\" but there's often a contrast between \"noise\" and \"signal\" but some of these have signal for important processes (especially thi...,