Challenge assumptions in data

Most data interpretations are built on unstated assumptions that can significantly skew conclusions. The best analysts deliberately question these assumptions to validate their findings and prevent misleading conclusions.

Key assumptions to challenge in any data analysis include:

• Correlation implies causation (it rarely does)
• The metrics actually measure what we think they measure
• The sample represents the entire population accurately
• Historical patterns will continue in the future
• All relevant variables have been considered

For example, a rising conversion rate might be interpreted as improved product-market fit, when it actually resulted from a change in traffic sources bringing more qualified leads. By explicitly questioning "What assumptions are we making?" you can avoid drawing incorrect conclusions that lead to misguided strategies.