What assumptions must be validated in advanced analytics?

< 1 min read

In advanced analytics, I first validate data quality assumptions like completeness, accuracy, and consistency.
For example, I check if there are missing values or duplicate records before building any model.
I validate normality assumptions when using regression or statistical tests.
I also check independence of observations to avoid biased results.
In forecasting projects, I verify seasonality and trend assumptions using historical data.
For classification models, I check class imbalance to avoid misleading accuracy.
I validate that sample data represents the overall population to prevent skewed insights.
Validating these assumptions ensures reliable outputs and builds stakeholder trust in the analysis.

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