<p>This paper focuses on block likelihood estimation for geostatistical data, a method that balances statistical accuracy and computational efficiency. Central to this approach is the choice of block size, which can significantly impact performance. This study contributes by providing a thorough numerical investigation of the effects of large versus small block configurations. Findings from both simulation experiments and real-data analyses of sea surface temperature challenge the prevailing assumption that larger block sizes invariably lead to improved statistical performance.</p>