Market Research: Foundations to Practice · Analyzing and Interpreting Data
Avoiding Common Interpretation Errors
Several specific interpretation mistakes recur often enough across the field to deserve explicit naming, so researchers can recognize them in their own draft conclusions before a report ever ships. This chapter names four of the most common.
As introduced in Module 2's discussion of causal research, observing that two variables move together does not establish that one causes the other; a third, unmeasured variable might actually be driving both, or the presumed direction of causation might simply be reversed from what a researcher's initial intuition assumed, and only a genuine causal research design, not a correlational one, can meaningfully distinguish between these real possibilities.
- Correlation doesn't establish causation; a third unmeasured variable or reversed causal direction are real possibilities only a genuine causal design can rule out.
- Confirmation bias can operate unconsciously; pre-registering analysis plans and actively seeking disconfirming evidence help guard against it.
- Findings from a non-representative sample shouldn't be generalized with full confidence; slippage from qualified methodology language to bold executive summary language is a common failure.
- A single study, however rigorous, carries inherent limitations; treating it as fully definitive overstates what any one research effort can honestly deliver.