Data errors are ubiquitous in tables. Extensive research in this area has resulted in a rich variety of techniques, each often targeting a specific type of errors, e.g., numeric outliers, constraint violations, etc. While these diverse techniques clearly improve data quality, it places a significant burden on humans to configure these techniques with suitable rules and parameters for each data set. For example, an expert is expected to define suitable functional-dependencies between column pairs, or tune appropriate thresholds for outlier-detection algorithms, all of which are specific to one individual data set. As a result, users today often hire experts to cleanse only their high-value data sets. We propose sj, a unified framework to automatically detect diverse types of errors. Our approach employs a novel 'what-if' analysis that performs local data perturbations to reason about data abnormality, leveraging classical hypothesis-tests on a large corpus of tables. We test sj on a wide variety of tables including Wikipedia tables, and make surprising discoveries of thousands of FD violations, numeric outliers, spelling mistakes, etc., with better accuracy than existing algorithms specifically designed for each type of errors. For example, for spelling mistakes, sj outperforms the state-of-the-art spell-checker from a commercial search engine.