Aliasing is a phenomenon that can occur due to improper sampling or data collection, where the original signal is indistinguishable when reconstructing data from the collected samples. This usually occurs because the sampling rate and/or resolution is inadequate.
Figure 1 shows an example of aliasing. The original signal (shown as a solid line) has a frequency of 0.9Hz and is being sampled at a rate of 1.0Hz. The sampling data is represented by the green dots. By reconstructing the data from the samples collected, a signal with a frequency of 0.1Hz (shown by the dashed line) is generated. Obviously this signal is not an accurate representation of the original signal, and this demonstrates aliasing.
Aliasing can be prevented by using proper sampling rates and resolutions. The use of filters, which are available in many data collection software, can also help minimize or prevent the effects of aliasing.
Figure 1. An illustration of aliasing; the solid line is the original signal; the green dots represent sampling data; the dashed line is the aliased signal