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Data Literacy

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Data literacy is the capacity to find, evaluate, interpret, and critically analyze quantitative information — reading charts and graphs with structural accuracy, understanding the statistical concepts (mean, median, variance, confidence intervals, correlation, causation, sample size, base rates) that determine what data can and cannot support, identifying common data visualization misleading patterns, and recognizing the difference between statistical significance and practical significance.

Role

Data is the currency of modern decision-making across every domain — medicine, policy, business, science, journalism — and data illiteracy is its most exploited vulnerability. The majority of people cannot reliably distinguish between correlation and causation in a presented finding, cannot recognize when a chart's y-axis is manipulated to exaggerate a trend, cannot evaluate whether a sample size supports a stated conclusion, and cannot assess whether statistical significance translates into any practical effect worth acting on. This is not a marginal problem: it means that most people are making consequential decisions — health choices, investment decisions, political positions, business strategies — from quantitative evidence they cannot accurately evaluate, in an information environment full of deliberately misleading data presentations by parties with strong incentives to produce them.

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