If the data includes NYSE or TORQ database features, note how specific trading procedures (like trade reversals) affect the results. To give you a more precise outline, could you clarify:
In some academic contexts, "Bias" refers specifically to errors in trade classification models. If your paper is about market microstructure:
Detail the "artifacts" found inside. Look for registry keys, hidden directories, or encrypted strings that point to the "Bias" theme. Conclusion: What does the evidence prove? Option 2: Data Science / AI Bias Analysis
Use visualizations like histograms or heatmaps to show where the "bias" exists in the data.