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What is Business Case Proof?

Businesspeople are usually intent on proving something when they deliver business case results.

Delivering proof is the motivation and intent behind many if not most case building efforts. Business people delivering case reports as well write in the opening lines,

I aim to prove ...
     ... financial justification for my proposal.
     ... funding my project is a sound business decision.
     ... my proposal is the best available course of action.
     ... I acted wisely and responsibly in taking this action."

Case builders intent on proving something often go on to produce the return on investment (ROI), internal rate of return (IRR), or net present value (NPV) they expect from their proposals. These financial metrics may be very attractive, but for some reason, they just don't "make the case" with CFOs, review boards, or other senior management. It's not unusual to find doubts all around that anyone can truly prove anything with a business case.


In brief, not everyone in business is aware that business case analysis can indeed deliver a very strong form of proof—but only when case builders and stakeholders alike understand the nature of business case reasoning and how to present business case evidence.

Solid, compelling business case proof has very little to do with finance, and quite a lot to do with understanding the scientific method. Sections below explain how case builders prove their case using the same kind of reasoning that makes possible proof in physics and chemistry laboratories. Those familiar with laboratory research will recognize immediately that business case proof has much in common with the cause-and-effect proof from a controlled experiment in the lab.

Explaining Business Case Proof Concepts in Context

This article explains business case proof rationale—the same reasoning that supports proof in the science laboratory. Discussions below explain proof reasoning in context with related terms and concepts from statistics, experimental design, and risk analysis.

Experimental Test
Business Case Scenario
Cause and Effect
Experimental Hypothesis
Experimental Effect
Scientific Method
Statistical Significance
Risk Factor
Risk Analysis
Sensitivity Analysis
Confidence Interval
Beyond Reasonable Doubt
Controlled Experiment


How Controlled Experiments Deliver Proof
Proof by Scientific Method

In chemistry, physics, and other kinds of laboratory research, the scientist tests the idea that one factor causes another, or that one theory accounts for reality better than another, with a controlled experiment.

The research scientist first creates two or more experimental test conditions. Throughout the experiment, all receive exactly the same treatment in all ways, except for one or more experimental manipulations. After completing all experimental trials, when different outcomes appear in the different test conditions, the experimenter uses statistical probabilities to argue "only reasonable explanation" for the difference is the experimental manipulation--some other factor, and not random variability in test results. The lab researcher writes:

Conclusion proven:
The experimental manipulation caused the different outcomes!

Laboratory Example: Proving Penicillin Kills Bacteria

It is one thing to suspect that one factor impacts another. But suspicion is not proof. Proof that penicillin kills certain bacteria, for example, was a two-step process: Suspicion first, then proof.

Suspicion Comes First: The Observation

Alexander Fleming receives credit for discovering the efficacy of penicillin in 1928. Fleming was a physician and research microbiologist working at St. Mary's hospital in London. Fleming was searching for ways to combat an especially virulent strain of Staphylococcus bacteria. The bacteria—Staphylococcus aureus—was the known cause of especially destructive post-operative infections.

As part of this research, Fleming was maintaining bacterial cultures in the lab, growing in open dishes. He was surprised to discover, one morning, that a few of the cultures were contaminated with small deposits of a certain fungus mold—Penicillium notatum—that had no-doubt drifted in through the open window. The especially striking finding was that staphylococcus bacteria surrounding the mold had died. At the same time, bacteria in cultures that did not have mold were still thriving. This observation gave Fleming the strong suspicion that this particular mold was effective in killing bacteria.

Proof Comes Second: The Controlled experiment

Before taking Penicillium from the laboratory into the clinic, Fleming needed proof that this particular fungus caused bacteria death, not something else. Proof, in such cases, requires an experimental control (actually a series of quite a few experimental tests).

The experimental design for each test, however, was straightforward: Fleming separated a group of healthy bacterial culture dishes into two groups: (1) Cultures in one group received a small deposit of Penicillium notatum, while (2) Cultures in the other group received a small deposit of another mold. Other than that, all cultures were allowed to grow under the same conditions for a period.

Throughout, researchers monitored both groups for bacterial growth and longevity. Because these experiments were controlled in this way, the only reasonable explanation for the effect for dead bacteria in the Penicillium cultures was the presence of Penicillium. It was reasonable to conclude, therefore, that Penicillium kills Staphylococcus aureus bacteria.

Scientific Method in Business Case Proof

By the same kind of reasoning, the business case author “proves” that one proposal or another is the better choice for action by comparing two or more carefully designed scenarios. Generally speaking, a scenario is an account, or story, that describes what happens under one course of action.

Comparing Scenarios Instead of Test Conditions

Business case scenarios—just like test conditions in the lab—are identical in all respects except for one or a very few proposed actions. The business case author analyzes outcomes in each scenario in business terms, including:

  • Financial metrics such as Total Cost, Net Cash flow, NPV, ROI, and IRR.
  • impacts on key performance indicators (KPIs) for nonfinancial outcomes.

Proof Lies in the Comparison

If the business outcomes in a scenario called "Implement the Proposal" differ from outcomes in another scenario, "Business as Usual," and if the differences are important, large enough to matter, and if it is statistically unlikely the differences are due to random variability, the business case author may write:

Conclusion proven:
"Proposal scenario" actions lead to better business outcomes than continuing under "Business as Usual."

Proof Beyond a Reasonable Doubt

The business case writer—just like the laboratory scientist—provides statistical evidence that the different scenario outcomes are not due simply to random variability in assumptions underlying cost/benefit estimates. For the business case author, risk and sensitivity analyses serve this purpose: For instance, the author may produce confidence interval estimates such as these:

  • The 95% confidence interval for net gains under the proposal scenario is $8.00 million to $12.00 million.
  • The 80% confidence interval for the 5-Yr ROI under the proposal scenario,
    is 26% to 34%.

To make such proof credible, the writer must show that different scenarios belong to a "controlled experiment," that is, the case builder created and analyzed all scenarios objectively by the same cost-benefit rules. The author's case report—just like the chem lab report—has a "Methods" section with the cost model, benefits rationale, scope and boundary statements, and major assumptions under each scenario.

Like a good trial lawyer summing up a case, the business case author finishes by taking the business case "jury" once more through the proof rationale:

I believe you will decide that funding my proposal is the better business decision for this company!
  • Better is a relative term.
    The results show in these results that proposal outcomes are better, relative to "Business as Usual" outcomes, using our key financial metrics and KPI's.
  • These results show that scenario comparisons are objective and fair.
  • This analysis shows that the uncertainty in assumptions underlying forecast outcomes is small and acceptable.

For a concise introduction to business case proof and the role of case design, risk, and sensitivity analysis, see 5th Edition of Business Case Essentials: