Data Interpretation

Data interpretation is the process of reviewing data, identifying patterns, and drawing meaningful conclusions. Raw numbers on their own rarely tell a story — data interpretation is what turns them into actionable information. Whether you’re reading a patient’s lab results, reviewing monthly sales figures, or analyzing survey responses, the skills are the same: read accurately, calculate key values, compare across categories, and spot what matters.

Reading Multi-Column Data Tables

Most real-world data comes in tables with multiple columns. The key to reading them accurately is understanding what each column represents and how the rows relate to one another.

Example 1: Weekly Sales by Department

Step 1: Calculate the row totals.

$\text{Electronics} = 1200 + 980 + 1050 + 1100 + 2400 = \$6{,}730$

$\text{Clothing} = 800 + 750 + 720 + 690 + 1500 = \$4{,}460$

$\text{Grocery} = 3100 + 2900 + 3000 + 3050 + 4200 = \$16{,}250$

Step 2: Calculate the grand total.

$\text{Grand Total} = 6730 + 4460 + 16250 = \$27{,}440$

Step 3: Find each department’s share of total sales.

$\text{Electronics share} = \frac{6730}{27440} \times 100 \approx 24.5\%$

$\text{Clothing share} = \frac{4460}{27440} \times 100 \approx 16.3\%$

$\text{Grocery share} = \frac{16250}{27440} \times 100 \approx 59.2\%$

Conclusion: Grocery drives nearly 60% of total weekly revenue. All three departments saw a significant spike on Friday, suggesting end-of-week shopping patterns.

Calculating Percentages from Raw Data

Converting raw numbers to percentages makes comparison easier, especially when the totals differ across groups.

Example 2: Student Test Results

A class of 40 students took an exam. The score distribution was:

What percentage of students scored 80 or above?

$\text{Students scoring 80+} = 8 + 14 = 22$

$\text{Percentage} = \frac{22}{40} \times 100 = 55\%$

Answer: 55% of students scored 80 or above.

What percentage scored below 70?

$\text{Students below 70} = 5 + 3 = 8$

$\text{Percentage} = \frac{8}{40} \times 100 = 20\%$

Answer: 20% of students scored below 70.

Identifying Trends Over Time

When data is collected at regular intervals, you can look for trends — consistent increases, decreases, or turning points.

Example 3: Monthly Revenue Trend

Month-over-month change:

$\text{Jan} \to \text{Feb}: \frac{44500 - 42000}{42000} \times 100 \approx +6.0\%$

$\text{Feb} \to \text{Mar}: \frac{47200 - 44500}{44500} \times 100 \approx +6.1\%$

$\text{Mar} \to \text{Apr}: \frac{46800 - 47200}{47200} \times 100 \approx -0.8\%$

$\text{Apr} \to \text{May}: \frac{49100 - 46800}{46800} \times 100 \approx +4.9\%$

$\text{May} \to \text{Jun}: \frac{52300 - 49100}{49100} \times 100 \approx +6.5\%$

Interpretation: Revenue shows a strong upward trend overall. April was a slight dip ($-0.8\%$), but growth resumed immediately. This single-month dip is likely seasonal or temporary — it does not indicate a negative trend.

Spotting Outliers

An outlier is a data point that is significantly different from the rest of the data. Outliers can signal errors, unusual events, or important findings.

In Example 1 above, Friday sales in Electronics ($2,400) were more than double the midweek average of roughly$1,080. That’s an outlier worth investigating — was there a sale event? A product launch? Outliers should be identified, but not automatically removed. Always ask why the value is different.

Real-World Application: Nursing — Interpreting a Patient’s Lab Results

A nurse reviews a patient’s complete blood count (CBC) results over three days:

Step 1: Compare each value to the normal range.

• WBC: Day 1 (11.2) is already slightly above the normal upper limit of 11.0. By Day 3 (16.8), it is well above normal.
• Hemoglobin: Day 1 (13.1) and Day 2 (12.4) are normal. Day 3 (11.8) has dropped below the normal lower limit of 12.0.
• Platelets: All three days are within normal range.

Step 2: Identify trends.

• WBC is rising steadily: $11.2 \to 13.5 \to 16.8$. That’s a $50\%$ increase over three days.

$\text{WBC change} = \frac{16.8 - 11.2}{11.2} \times 100 = 50\%$

• Hemoglobin is falling: $13.1 \to 12.4 \to 11.8$, a $9.9\%$ decrease.

Step 3: Draw conclusions.

Rising WBC combined with falling hemoglobin suggests a possible infection or bleeding event. The nurse should flag this pattern for the physician immediately — neither value alone might seem alarming, but the trend across both tests tells a critical story.

Data Interpretation Reference

Practice Problems

Test your understanding with these problems. Click to reveal each answer.

Key Takeaways

• Read tables carefully — identify what each column and row represents before doing any calculations
• Convert raw numbers to percentages to make comparisons meaningful, especially when totals differ
• Look for trends across time by computing percent changes between periods
• Outliers deserve investigation, not automatic removal — ask why the value is different
• Combine multiple data points to draw conclusions — a single value in isolation can be misleading, but patterns across values tell a story

Return to Statistics for more topics in this section.