A new method called the Hurst-Kolmogorov (HK) method is better than the current gold standard, Detrended Fluctuation Analysis (DFA), for estimating the Hurst exponent in behavioral sciences. The HK method accurately assesses long-range correlations in short time series, has less variability, and gives consistent estimates regardless of time series length. This means that using the HK method is more reliable for analyzing real-world data in behavioral sciences.