What are some common sources of uncertainty in quantitative analytics and how can you reduce them?

??Common sources of uncertainty in quantitative analytics include measurement errors, missing data, outliers, and sampling errors. ???To reduce uncertainty, start with thorough data cleaning to eliminate errors and inconsistencies. ??Use data validation and imputation techniques to address missing values. ??Normalize or transform data to ensure consistency and comparability across datasets. ??Apply exploratory data analysis (EDA) to identify outliers and unusual patterns that may introduce bias. ??Employ robust statistical methods that account for data variability and uncertainty. ??Regularly review and update data collection processes to improve accuracy and reliability.

Data uncertainty poses significant challenges in quantitative analytics, arising from missing values, measurement errors, and outdated information. For instance, financial forecasters often grapple with gaps in historical data, which can distort predictions, while inaccurate sensor data in IoT applications leads to flawed insights. Tackling these issues requires meticulous data cleaning, advanced imputation techniques to address missing values, and robust statistical models that account for measurement errors. Continuous validation and updating processes ensure data accuracy and relevance, thereby minimizing uncertainty and enhancing the reliability of analytical results.

In my experience data uncertainty, and more specifically lack of confidence in data quality, is the largest source of uncertainty in QA. The old adage applies, "Garbage IN, Garbage OUT". In this regard, I think quality in QA for any organization is critically dependent on Data Governance. All the mentioned techniques to resolve uncertainty are valid and necessary, but applying these practices also introduces the possibility of new bias and additional error.

Validation techniques, such as cross-validation, can be used to assess how well your model generalizes to new data. Calibration is another important method, as it ensures that the predictions from your model are consistent with observed outcomes. Regularization techniques, such as Lasso or Ridge regression, can also be utilized to prevent overfitting by imposing penalties on model complexity. Diagnostic tests play a crucial role in identifying potential issues within your model, allowing for adjustments to be made. Sensitivity analysis can be particularly useful in understanding how variations in model inputs impact the outputs, thereby offering insights into the robustness of your model.

In order to make sure that a statistical model is adequate and coefficients are reliable, one needs to identify all the probabilistic assumption in terms of the observed data and test them using misspecification tests. If the assumptions are not valid the estimators are not consistent and the hypothesis testing error probabilities is different in reality than theory

Key source of uncertainty stems from model assumptions and simplifications. Simplified relationships between variables or narrow data samples can miss important real-world dynamics. Leveraging personal experience and opinions from SMEs to sense-check assumptions is crucial. I also find statistical significance testing invaluable for quantifying model reliability. Overall though, thoughtful data cleaning, establishing monitoring guardrails, and constantly reevaluating methodology with a critical eye are the best ways I've found to reduce uncertainty.

When you backtest your trading strategy applying it only on the historical data, you tested one possible scenario through an infinite of possible scenarios. To reduce the uncertainty about the strategy performance, you need to test several paths that the past could be to not obtain one Sharpe ratio for example but a distribution of the Sharpe ratio value. To do so, you can use a purged cross validation or a Monte Carlo simulation for example!

One thing I found helpful is to socialize the limitations of the quantitative analysis we perform with stakeholders and doing sensitivity analysis addressing those limitations