Aliasing occurs when a signal is undersampled, causing high-frequency components to masquerade as lower frequencies. Which statement best describes aliasing?

• A. Aliasing is avoided by filtering before sampling at all frequencies.
• B. Aliasing occurs when high-frequency components masquerade as lower frequencies due to undersampling; observed as distortions in the reconstructed signal.
• C. Increasing the sampling rate has no effect on aliasing.
• D. A higher sampling rate always eliminates aliasing completely.

Answer: (B)

Aliasing shows up when you don’t sample fast enough to capture the highest frequency content of a signal. When the sampling rate is too low, those high-frequency components fold back into lower frequencies in the sampled data, creating distortions that appear in the reconstructed signal. That idea is exactly what the statement is saying: high-frequency parts masquerade as lower frequencies because of undersampling, and you can observe it as distortions after reconstruction.

In practice, anti-aliasing filters are used before sampling to reduce content above half the sampling rate, so folding doesn’t occur. But you can’t rely on filtering to “cover all frequencies” in the sense implied by that choice, and simply increasing the rate doesn’t automatically solve every case of aliasing: if the signal truly fits below the Nyquist limit, higher sampling rate helps, but non-idealities and content beyond the chosen limit can still cause aliasing. The statement captures the essence of aliasing most precisely: the masquerading of high-frequency components due to undersampling and the resulting reconstruction distortions.