When tackling discrepancies in historical data analysis, a meticulous approach is essential. I’ve often found that discrepancies stem from missing context, poor data quality, or changes in data collection methods over time. My strategy begins with understanding the origin of the data: I engage domain experts to clarify anomalies and assess whether discrepancies are errors or meaningful shifts in trends. Advanced imputation techniques, such as using predictive modeling, can help fill gaps without introducing bias. Finally, I employ version control and maintain detailed documentation to ensure all modifications are traceable, fostering both transparency and trust in the analysis.

The impact of inconsistencies can range from moderate to severe as populations and data generally are comprehensive or very large. Thus statistics such as mean and mode are less impacted than maxes or min values. However, large discrepencies there can affect mean values or standard deviation which in turn affects predictive analytics, for example. The methodology we have used involves predicting the range of risk involved in assessing the impact of a discrepancy such as Monte Carlo Analysis which can be modeled with Crystal Ball among others. Machine Learning libraries in python like Pyspark address this. The imputer, the K-Nearest Neighbor Imputer, and Linear Regression class allow users to fill in missing data rather than drop it.