In the selection of horizontal parameters for analysis, what should the sampling rate be?

The correct choice emphasizes that the sampling rate should be at least two times greater than the highest frequency in the signal. This principle is grounded in the Nyquist Theorem, which states that in order to accurately reconstruct a continuous signal from its samples, the sampling rate must be at least twice the maximum frequency component of that signal.

This requirement is crucial because if the sampling rate is lower than this threshold, aliasing can occur, which means that higher frequency signals can be misrepresented as lower frequency signals when sampled, leading to distortion and loss of information. By ensuring the sampling rate is at least double the highest frequency, the data captured allows for an accurate representation of the original signal, thus preserving its integrity for analysis.

Contextually, other options do not correctly align with this fundamental principle. For instance, suggesting that the sampling rate be equivalent to the highest frequency would not allow for adequate data representation, risking significant loss of important information. Proposing it be at least twice the Nyquist frequency misunderstands the relationship, as Nyquist frequency is itself defined as half the sampling rate. Lastly, approaching Nyquist frequency does not provide a sufficient buffer to prevent aliasing, further underscoring the necessity of the choice that maintains the two-times standard.