Data Literacy’s core isn’t knowing how to code — it’s having four capabilities: ① **Reading Data**: correctly interpreting charts, reports, and statistical summaries; ② **Working with Data**: understanding data collection methods, limitations, and common biases; ③ **Analyzing Data**: using basic tools (Excel, simple visualizations) for preliminary data analysis; ④ **Questioning Data**: identifying assumptions and potentially misleading elements behind data. These four dimensions form the basic “Data Citizen” competency framework.
## Most Common Data Interpretation Errors
**Confusing correlation with causation**: two variables correlating (moving in the same direction) doesn’t mean one causes the other. Ice cream sales and drowning rates are positively correlated — their shared cause is summer heat, not ice cream causing drowning. Common workplace error: interpreting “sales rose during the advertising period” as a direct advertising effect while ignoring seasonal factors, competitive environment changes, and other confounding variables. **Survivorship bias**: a systematic error from seeing only successful samples while ignoring failures. “Our company’s most successful salespeople all have optimistic personalities” — but we didn’t observe equally optimistic salespeople who didn’t succeed. **Ignoring base rates**: growth rate numbers are misleading without context. Growing from 1 to 3 people is “200% growth”; growing from 1,000 to 1,100 is “10% growth” — two numbers with completely different practical meanings. **P-value misuse**: statistical significance (P<0.05) doesn't equal practical significance (whether the effect size has real-world meaning). ## Excel Data Analysis Core Skills Excel remains the most widely used workplace data tool. Core functions: **Pivot Tables**: the essential tool for summarizing large amounts of data by dimensions — mastering pivot tables is the first step in data literacy; **VLOOKUP/XLOOKUP**: cross-table data lookup; **Conditional Formatting**: a quick data visualization tool; **Basic statistical functions** (AVERAGE, MEDIAN, STDEV, CORREL); **Chart selection**: different data types suit different chart types (trends use line charts, composition proportions use pie charts, distribution uses histograms, comparisons use bar charts, correlation uses scatter plots). See [SQL Data Query Basics](https://sunqi.org/sql-data-query-basics-en/) and [Data Visualization Principles](https://sunqi.org/data-visualization-principles-en/).
