Start with the worked examples to see complete reasoning, then use the shorter pattern library for variation. Level guidance and frameworks show how the same task changes as the evidence, audience, or assignment becomes more demanding.
A strong graph description gives readers the overall pattern first, then uses a few well-chosen values, comparisons, peaks, changes, or anomalies to support that summary without narrating every point on the chart or inventing causes the graph cannot establish.
Identify the graph type, variables, time period, categories, and units.
State the overall trend or dominant comparison before listing details.
Select values that prove the pattern: start/end, high/low, largest gap, crossing point, or anomaly.
Use precise comparison language such as rose from, remained stable, exceeded, narrowed, or fluctuated.
Separate observation from interpretation and label uncertainty when the data do not establish a cause.
Worked format lab
See complete reasoning, not just isolated lines
Use these fuller examples to see what changes between a recognizable pattern and a finished piece of writing. The examples are original or explicitly illustrative, so they demonstrate structure without inventing real-world evidence.
Worked example 1Line graph
Illustrative data: monthly support tickets — Jan 120, Feb 128, Mar 131, Apr 166, May 138, Jun 136.
The line graph shows monthly support tickets during the first half of the year. Ticket volume is relatively stable from January through March, rising only from 120 to 131. April is the clear exception, when the total jumps to 166, before falling back to 138 in May and 136 in June. Overall, the graph shows a temporary spike rather than a sustained upward trend.
Why it works: The paragraph gives the overview first, selects start/peak/end values, and does not invent a cause for April.
Worked example 2Bar graph
Illustrative data: completion rate — Team A 78%, Team B 91%, Team C 84%, Team D 69%.
The bar chart compares completion rates across four teams. Team B records the highest rate at 91%, followed by Team C at 84% and Team A at 78%. Team D is lowest at 69%, leaving a 22-percentage-point gap between the highest and lowest teams.
Why it works: The description prioritizes ranking and the largest meaningful gap rather than listing numbers without comparison.
Worked example 3Pie chart
Illustrative data: budget — housing 38%, food 19%, transport 14%, savings 17%, other 12%.
Housing represents the largest share of the illustrative budget at 38%. Food is the second-largest category at 19%, while savings accounts for 17%. Transport and other costs together make up the remaining 26%. The distribution is therefore dominated by housing, which is twice the size of the food category.
Why it works: The paragraph describes composition and comparison; it does not claim whether the allocation is good or bad.
Worked example 4Scatter plot
Illustrative pattern: practice minutes and quiz score show an upward cloud with several outliers.
The scatter plot shows a generally positive association between practice time and quiz score: observations tend to appear higher on the score axis as practice minutes increase. The points are not tightly clustered around one line, and several outliers remain, so the relationship appears moderate rather than deterministic. The plot alone cannot establish that additional practice caused the higher scores.
Why it works: The analysis distinguishes visual association, strength, outliers, and causation.
Worked example 5Histogram
Illustrative distribution: most reading sessions fall between 20 and 50 minutes, with a long right tail.
The histogram is concentrated between 20 and 50 minutes, where most reading sessions occur. Frequencies become progressively smaller above 50 minutes, but a few sessions extend beyond 90 minutes, producing a right-skewed distribution. The most useful summary is therefore the concentration and tail shape rather than the duration of any single session.
Why it works: The paragraph describes distribution shape instead of treating histogram bars as unrelated categories.
Depth by level
Increase the reasoning, not just the word count
Level
What changes
Quality test
Basic data description
Name the graph, variables, units, and obvious high/low or rise/fall pattern.
Accuracy is more important than sophisticated interpretation.
Academic / report writing
Lead with an overview, select supporting values, compare groups or periods, and note anomalies.
Avoid narrating every point.
Analytical commentary
Separate observation from explanation, consider scale and missing data, and qualify causal claims.
Explain what the display can and cannot establish.
Reusable frameworks
Start from the decisions the format requires
Framework 1
Observation → function
1. What can the viewpoint actually perceive?
2. Which 1–2 details matter now?
3. What do those details change in image, pace, relationship, or action?
4. What interpretation remains uncertain?
Line graph: “Enrollment rose from 420 students in 2021 to 560 in 2024, with the largest year-to-year increase occurring between 2022 and 2023.”
2
Bar graph: “The East region recorded the highest satisfaction score at 84%, nine percentage points above the West region.”
3
Pie chart: “Housing accounts for 38% of the illustrative monthly budget, making it the largest single category.”
4
Scatter plot: “The points show a moderate upward association: higher practice time generally coincides with higher scores, although several observations fall away from the overall pattern.”
5
Histogram: “Most observations fall between 40 and 60 minutes, while relatively few occur above 80 minutes.”
6
Two-line comparison: “Both series rise over the period, but Series A grows faster after Q2 and ends 14 units above Series B.”
7
Plateau: “After increasing through May, the measure remains close to 72–74 for the final three months.”
8
Volatility: “The monthly total fluctuates between 110 and 165 rather than following a steady upward or downward trend.”
9
Anomaly: “April breaks the otherwise gradual pattern, falling to 48 before the series returns to the previous range in May.”
10
Percentage-point wording: “The response rate increased from 62% to 71%, a rise of nine percentage points.”
11
Cautious interpretation: “The graph shows that the two measures move together in this sample; it does not by itself establish that one causes the other.”
12
Data-quality note: “Because no value is reported for June, the apparent change from May to July should not be described as a continuous two-month trend.”
Turn an example into your own writing
Keep the underlying decision or pattern, then replace the subject, evidence, relationship, constraints, and tone with details that belong to your situation. If your final line still works after swapping only one noun, it may be too close to the example.