Proprioceptive sensor data, including inertial measurement units (IMU), joint encoders, and torque sensors, plays a critical role in state estimation for quadruped robots operating in dynamic and unstructured environments. However, these signals are often degraded by various sources of error, such as high-frequency noise, bias, drift, and contact-induced disturbances, which directly affect estimation accuracy and stability. This study presents a systematic analysis of sensor-specific noise characteristics and evaluates the effectiveness of preprocessing methods tailored to each sensor modality. Specifically, moving average filtering is applied to encoder signals to mitigate noise amplification during differentiation, while first-order low-pass filtering is employed for IMU and torque signals to suppress high-frequency noise. Experimental results on a publicly available quadruped dataset demonstrate that encoder velocity RMSE is reduced by 12.09%, high-frequency energy decreases by 59.63%, and signal-to-noise ratio (SNR) improves by 145.6%. However, variance reductions remain limited (3.39% for IMU and 4.05% for torque), indicating the persistence of impulsive, non-Gaussian noise caused by contact events. These findings highlight that linear preprocessing methods are effective for attenuating high-frequency noise but insufficient for handling non-Gaussian disturbances. The study provides practical insights into the effectiveness and limitations of preprocessing strategies, serving as a foundation for developing more robust signal processing and state estimation frameworks in quadruped robotics.