  In all measurement campaigns, one needs to assert that the instrumentation
tools do not significantly impact the system being monitored. This is critical
to future claims based on the collected data and is sometimes overseen in
experimental studies. We propose a method to evaluate the potential "observer
effect" of an instrumentation system, and apply it to the OMF Measurement
Library (OML). OML allows the instrumentation of almost any software to collect
any type of measurements. As it is increasingly being used in networking
research, it is important to characterise possible biases it may introduce in
the collected metrics. Thus, we study its effect on multiple types of reports
from various applications commonly used in wireless research. To this end, we
designed experiments comparing OML-instrumented software with their original
flavours. Our analyses of the results from these experiments show that, with an
appropriate reporting setup, OML has no significant impact on the instrumented
applications, and may even improve some of their performances in specifics
cases. We discuss our methodology and the implication of using OML, and provide
guidelines on instrumenting off-the-shelf software.
