Variance & Driver Trees: 7 Key Terms

By Devon Coombs, CPA, MBA · Teaching Professor of Finance, Santa Clara University · Reviewed August 2026

Variance analysis explains why actual results differ from plan by decomposing a change into components such as price variance, volume variance, and mix effect. A driver tree organizes that decomposition by linking a headline metric to the operational inputs behind it, while concepts such as the alternative hypothesis and falsifiability set a standard for which explanations count as tested rather than assumed. The decision-quality layer connects this analysis to action, since a variance explanation is typically only as useful as the decision it supports, a discipline developed in Module 3 of the free Applied AI for Finance and Accounting course.

These terms are taught in Module 3: Variance Analysis and Driver Trees of the free Applied AI for Finance and Accounting course; the full course glossary collects every chapter in one place.

Alternative hypothesis: A competing explanation for an observed result, offered alongside the primary one to guard against a confidently wrong single story.
Decision-quality layer: The step that turns an explanation into a recommendation by adding alternative hypotheses and the evidence that would distinguish them.
Driver tree: A structured breakdown of a result into the underlying drivers that moved it, such as splitting a revenue change into price, volume, mix, and cost effects.
Falsifiable: Framed so that specific evidence could confirm or rule it out. A hypothesis you cannot test is not decision-useful.
Mix effect: The part of a change caused by a shift in the proportion of higher- or lower-value items sold, separate from total volume.
Price variance: The part of a revenue change caused by a change in price, measured as the price change times the actual volume.
Volume variance: The part of a revenue change caused by a change in units sold, measured as the volume change times the budgeted price.

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Put the vocabulary to work: the free calculators and decision guides apply these terms, and the free Applied AI for Finance and Accounting course teaches them in context.