Knowing if your analysis is wrong is about being brutally honest with yourself, methodical in your process, and open to scrutiny. Here are clear signs your analysis might be off the mark—and how to catch them early:
If your conclusions contradict known facts, basic logic, or real-world behavior, step back.
Example: A financial model predicting negative profits for a clearly profitable company.
What to do: Rethink assumptions, double-check formulas or filters, and ensure you're not overfitting or overgeneralizing.
Too good to be true? It probably is.
Too chaotic to explain? Your model may be garbage-in, garbage-out.
What to do: Validate with a test set, or benchmark against trusted data.
If different methods (e.g., SQL vs. Excel vs. Python) give wildly different outputs on the same data, one or more are wrong.
What to do: Cross-validate your findings across tools or team members. Use logging and step-by-step tracing.
If your data types, units, missing values, or categories are mishandled, your analysis will fail before it even begins.
What to do: Do extensive data profiling before any analysis. Know your nulls, outliers, duplicates, and data lineage.
You built your analysis on guesses, or you're confirming a belief rather than testing a hypothesis.
What to do: List all your assumptions. Challenge them. Better yet, have someone else challenge them.
If peers can't follow your logic or replicate your work, it’s likely flawed or poorly communicated.
What to do: Document everything clearly. Version your work. Ask for a code or logic review.
You’re not comparing against a benchmark, industry norm, or known result.
What to do: Use past performance, dummy datasets, or expert estimates to test if your results are within reason.
✅ Final Advice:
Even seasoned analysts get it wrong sometimes. What separates a good one is how quickly they catch and correct it.