Usually not, except in some cases (considering you have mentioned 50Hz) there might be interferences from AC power network. It that case, sampling synchronously to the AC voltage turns such interferences in rather constant pedestal. But I guess accelerometer is not a subject to such interference.

How does the sampling frequency compare to the highest frequency in the “signal?”

After the long research and discussion, I have found this:

Some loss of data can occur, based on weighted average dynamics - if the weighted average points are not of a representative quantity.

Whilst the average of a entirely symmetrical and perfectly balanced set, indeed, would be partitioned correctly - You’re going to incur some small loss of information - depending on how spread out the points are.

You can counter-act that, with just increasing the interpolation points - But at that point - you’re incurring more costs of processing.

So, i guess the “side effect”, would mostly be a function of Interpolation points contra Accuracy contra parsing speed.

Another point of contention - that could arise - is representation of Co-ordinate points based on Dimensions.

Lower dimension points may inaccurately portray the dimensionality of the problematique - in so far as not being entirely accurate.

But in so far, as inferrence analytics goes and finding the average - sullying out averages from arbitrated interpolation points - enacts as a information strengthener - not a dampener.

Because it fullfills the process of Fisher Metrics of Aggregation - shuffling and re-sampling and running the process.

This is basically how most aggregate Simulation processes are meant to play out - like Monte Carlo and others.

You’re basically meant to aggregate to infinity and “parse in information as you go along” - so, shuffling and updating/resampling - just gives you more accuracy in so far as utilizing Bootstrapping (averaging runs).

So - i guess, the side-effect, would be increased accuracy of Sampling - as you’re better fullfilling the conditional of truly arbitrary sampling (in so far as to account for more information of the collective Distribution of the entire Metric space being analyzed).

But - other than that, you’re not really “losing” anything - in that sense.

Just adding to the process with more power and more data - takes more time.

At least, this is to my understanding of the problematique.