By: Samuel Samuel Ndungula
Statistical literacy begins with the ability to recognise when you do not know.
In data-driven environments, it is easy to be absorbed by sophisticated models, visualisations, and metrics.
Yet the most skilled analysts are those who recognise the boundaries of their knowledge and the uncertainty that underlies their conclusions.
True statistical literacy extends beyond building advanced models or interpreting p-values correctly. It is about asking deeper questions: Are the data representative? Do the assumptions hold? Do the results genuinely support the story being told?
Epistemic humility keeps us grounded. It reminds us that every dataset is an approximation of reality and that acknowledging uncertainty is not a weakness, but a mark of expertise.
But humility alone is not enough. Expertise also requires responsibility, which is the obligation to act with integrity when analysing or communicating insights. Recognising uncertainty must be followed by handling it responsibly.
Epistemic responsibility means being transparent about model limitations, questioning the quality of data sources, and resisting the temptation to oversimplify for convenience or persuasion. It calls for courage to admit what cannot be known, and care in how we present what can.
In a world where numbers shape strategic decisions, our influence extends beyond technical accuracy. It lies in how we communicate evidence, balance confidence with doubt, and sustain trust in the process of knowing.
