Interactive decision intelligence · built by Yisau Abdulwariz
Finding 1. Completion rate is 40.7% across 2,200 learner activity records.
Finding 2. Average learner progress is 75.0%; 27.9% meet the high-engagement rule used in this dashboard.
Finding 3. Product Analytics has the highest observed completion rate among courses.
Filter → compare KPIs → inspect charts → pivot → export.
Dataset: 2,200 rows · 10 fields.
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How the primary measure is distributed across the leading dimension.
Composition of the active population across the comparison dimension.
Directional movement of the primary measure across time or sequence.
Relationship between the primary measure and a supporting performance metric.
Distribution of the primary measure across the filtered population.
Distribution of the primary analytical measure across the active population.
Where the primary measure is concentrated across the second analytical dimension.
Average supporting performance by the third analytical dimension.
A second perspective on movement over the available time or sequence field.
Re-group the filtered population by rows, columns, measure and aggregation.
Top 500 filtered rows · CSV export
Estimate a two-sided, two-proportion test sample size before launching a controlled experiment. Planning estimates are not a substitute for a pre-registered analysis plan.
Assumes independent users, a binary outcome and equal allocation. Adjust for attrition, clustering, multiple comparisons and practical significance before launch.
Use this planning calculator to estimate a simple two-arm test. Inputs are illustrative planning assumptions—not observed results or a substitute for a formal power analysis.
Planning method: normal approximation for two independent proportions, equal allocation. Validate assumptions with a statistical package before running a consequential experiment; account for clustering, multiple tests, attrition and business guardrails.