The uncertainty of a measurement is not defined solely by the instrument of measurement, such as a sensor. As such, there is no complete answer to this question that Vernier can provide.
Experimental uncertainty is influenced by multiple factors, including elements having little to do with the sensor, such as the experimental design. This is why most scientists report the mean and standard deviation of the results of their experiment, not the accuracy of the device that they are using to do an experiment. Experimental uncertainty is much more important than sensor uncertainty in almost all cases.
Experimental uncertainty is readily determined by repeating the experiment several times and computing the mean and standard deviation of the results. Statistical tests can then be used to determine if the results are significant. In many cases, the class average and standard deviation can serve as an estimate of experimental uncertainty. Most statistical tests can be readily done in Excel or other spreadsheet programs.
For a full discussion on this topic, follow the link below to view Appendix C, “An Introduction to Uncertainty and the Use of Statistics in Biology” from Investigating Biology through Inquiry (BIO-I)
The uncertainty of a sensor measurement can still be estimated. Multiple measurements can give information on the precision of a sensor measurement. Often the precision is similar in magnitude to the resolution of the sensor. Estimating the accuracy of the measurement requires comparison to an external standard.
Information about the resolution of a particular Vernier sensor can be found for some sensors in the sensor user manual at https://www.vernier.com/support/manuals. In some cases the sensor may include accuracy information. In other cases the only practical way to assess accuracy is to do your own comparison to an external standard. For example, you might assess the accuracy of a pH measurement by comparing it to a pH standard.