From Raw Numbers to a Defensible Conclusion

What separates a summary from a conclusion

A summary restates what the data shows. A conclusion claims something about why it shows that, or what it means for a decision. Asking Good Analytical Questions gets you a specific, checkable answer. That answer is not yet a conclusion. "Revenue in the Northeast region grew twelve percent" is a summary, even though it is specific. "Revenue in the Northeast grew because of the new pricing tier" is a conclusion, and it makes a claim the numbers alone cannot settle.

The gap between the two is the actual work of analysis. A model can produce the first sentence from the data directly. The second sentence requires ruling out other explanations, which the model cannot do for you. That is why reaching a defensible conclusion takes more than one exchange with the tool.

Questions to ask the model before you trust its conclusion

Once a model offers a conclusion, not just a summary, ask it directly what else could explain the same numbers. A defensible conclusion survives this question. A model repeating its first answer in different words, without naming a real alternative, has not actually ruled anything out.

The next question presses harder: what would have to be true for the conclusion to be wrong, and whether the data says anything about it. A model that cannot describe a way its own conclusion could fail has not tested it, only asserted it.

A third question closes the loop: what is the smallest change to the data that would have produced a different conclusion. An answer naming a specific, plausible change shows real reasoning about the data's structure. An answer that cannot name one suggests the conclusion was never actually derived from the specific numbers in front of it.

A correlation-is-not-causation check before you accept a trend

Before accepting any conclusion that names a cause, run one check. Two things moving together does not mean one caused the other. Ask whether the model's explanation would survive a third factor moving both numbers at once, and whether it checked for one. That third factor might be a season, a promotion, or an unrelated event that happened at the same time. If the model never addresses whether one of these is the real explanation, its answer has not earned the word "because" it is using.

When to stop trusting the model and check the data yourself

At some point in this back-and-forth, verify a piece of the conclusion against the source data directly, rather than asking the model another question. Why Dumping a File Rarely Works and Code Interpreters vs. Plain Chat cover why this matters. A model's own account of its reasoning is not the same as the reasoning being correct. Every question so far in this section still runs through the model being questioned. The check that actually settles it does not. For the pricing-tier example above, pull the accounts that adopted the new tier yourself. Compare their revenue change directly against the accounts that did not, without asking the model to run that comparison for you. The model answering more questions convincingly is not the same as the underlying claim being true. A number you computed yourself, from the same rows, is.