Compute aggregates at the task interval for average comparisons
For average-comparison tasks over grouped time intervals, use discrete per-interval aggregate encoding on time-series charts to improve fidelity and mitigate errors caused by continuous aggregation for viewers comparing grouped results.
- purpose:refine
- basis:empirical
- task:compare
- scope:grouped-result
- time:time-interval
- data:temporal
- quality:fidelity
- lever:encoding
advice
Discrete interval aggregates
Compute and display the summary at the same interval the reader must compare. For example, add one average bar per month in a composite graph instead of relying on a continuous moving-average line when the question asks which month has the highest average.
reason
Why task-aligned aggregation works
Average comparison becomes easier when the chart already matches the grouping used in the question. Readers no longer need to mentally segment a continuous summary into monthly chunks before comparing them.
Mechanism: Discrete per-interval summaries align the visual unit of comparison with the task unit of comparison, reducing the extra work of extracting monthly values from a continuous smoothing line.
Evidence: For average comparisons, encodings that discretely aggregated the data per month outperformed the remaining encodings, and the paper highlights composite graphs as better matched to this task than a continuous moving-average display (Albers et al., 2014).
context
Use when the interval is known in advance
- User Goal: Decide which time interval has the highest average.
- Task: Compare summaries across fixed blocks such as months.
- Data: Quantitative time series with a known grouping level.
- Chart Setting: A chart where the designer can compute interval summaries before display.
- Success Criterion: Higher accuracy on interval-average comparisons.
exceptions
Do not use when the needed interval can change
Break it when: The reader’s comparison granularity is not known ahead of time or may shift across weeks, months, or other scopes. Why: The paper notes that discrete aggregation requires prior task knowledge and can misalign the chart with the reader’s actual scope.
costs
Costs of discrete aggregation
Sacrifice: You give up some flexibility for other temporal scopes and some raw local detail. Risk: A summary computed for months may not help with week-level questions. Mitigation: Keep the raw series visible underneath the interval summary when both local detail and interval averages matter.
mistakes
Common failure with this lever
Mistake: Add only a continuous moving-average line when the reader must compare averages for discrete time blocks. Why it fails: The reader still has to estimate where each block begins and ends and mentally recover a block-level average from a continuous curve.
check
How to test the choice
Failure Sign: Reviewers trace along a smoothing line instead of directly comparing one summary mark per interval. Quick Check: Show the same data once with per-interval averages and once with a moving average, then ask which interval has the highest average. Stronger Test: Repeat the A/B check across several datasets with different distractor intervals to confirm that the discrete version remains more accurate.
fix
What to change
- Replace a continuous moving-average overlay with one summary mark per task interval.
- Use bars or other discrete marks to encode the average for each interval.
- Overlay the raw line beneath the discrete averages if readers still need the original series.