I did not address the filter issue earlier.
Before looking for a filter or any other signal processing algorithm, it is important to characterize the noise. Is it white noise, pink noise, 1/f noise, or some kind of interference (such as power line frequency)? Does the noise vary significantly over time? Is it correlated with the signal? What is the minimum signal to noise ratio?
Next, look at the hardware parts of the system. Can the noise be reduced before being digitized? In your case check the scale manual. Many scales already do some filtering or averaging before generating an output. Is that filtering adjustable, possibly by changing the sampling rate?
Now you also need to characterize the desired signal. How does its bandwidth and amplitude compare to those of the noise? Does the signal have a complicated waveform which is important to the measurement or are simple DC or RMS measurements adequate? How many cycles of the signal are available for analysis at any particular time (for AC signals)?
What will you be doing with the results of the measurements? What effect does noise have on that? Are there certain kinds of errors which are more important than others? For example an excessive peak value might cause the system to damage expensive equipment while an error due to too aggressive averaging might only reduce efficiency by 1%.
After you have this information gathered, then you can begin to determine what kind of signal processing is most appropriate to reduce the noise. Sometimes a low pass filter is suitable, but in other cases it can be useless or even harmful.
Lynn