What issue can arise from aliasing in data acquisition?

Aliasing occurs in data acquisition when a signal is sampled at a rate that is insufficient to capture its variations accurately, specifically not meeting the Nyquist criterion, which states that the sampling rate should be at least twice the maximum frequency present in the signal. When aliasing occurs, higher frequency components of the signal can be mistaken for lower frequencies due to the sampling limitations. This misrepresentation leads to significant confusion and potential errors in interpreting data because the reconstructed signal does not accurately reflect the original signal.

Misinterpretation of data can result in incorrect conclusions about system behavior, performance, or health, which can lead to faulty diagnoses or adverse operational decisions. It’s crucial to recognize the importance of appropriate sampling rates in digital instrumentation to avoid these pitfalls and ensure accurate data representation.

In contrast, the other options do not capture the essence of aliasing. Increased data collection time does not inherently relate to aliasing; instead, aliasing typically results from inadequate sampling rates rather than lengthy collection periods. Improved signal quality is antithetical to the effects of aliasing, as aliasing degrades the perceived quality of the signal. Decreased calibration accuracy, while it could have some association with overall system errors, is not a direct consequence of aliasing. The primary