Interactive student lab
Build your controlled experiment plan
Draft your M-A-T-C-H experiment plan below. The live plan preview updates as you type and can be copied into Google Classroom, Canvas, or Docs before running the simulation.
Live Test Plan Preview
Direct answer
How do you run a fair business simulation experiment?
Write a decision question, record a baseline, and change one main input while holding the scenario, time period, and other important settings constant. Before seeing the result, name one primary outcome, two or three explanatory measures, a guardrail, and the result that would weaken your prediction. Then repeat the comparison or test a nearby setting before treating the difference as a pattern.
A high score is not automatically strong evidence. A useful experiment is reproducible, reports units and tradeoffs, distinguishes output from interpretation, and acknowledges what the simplified model omits. Use the method with any free xdage business simulation; no account or personal information is required.
Standardized baseline setup
1-Click scenario codes for fair, matched baseline comparisons
To ensure rigorous controlled experiments under the M-A-T-C-H method, students need identical starting parameters. All 18 simulators and companion worksheets feature 1-click scenario launch codes so everyone begins on the exact same city, cash, location, and term constraints without configuration drift.
Use the M-A-T-C-H fair-test method
M — Make a testable question
Name the decision, predicted direction, mechanism, and time horizon. “Will one more worker reduce waiting enough to cover added payroll?” is testable; “What is the best strategy?” is too broad.
A — Anchor a baseline
Save or copy every starting input, scenario condition, and result used for comparison. Label the run number and period. A remembered or partly changed baseline cannot support a clean claim.
T — Test one main change
Change price, staffing, capacity, quality, inventory, or promotion—not several at once. Hold other controllable settings constant so the intended mechanism remains visible.
C — Compare connected measures
Use one primary outcome plus drivers and guardrails. For example, compare profit with completed volume, wait time, payroll, quality, and cash rather than reporting revenue alone.
H — Hunt for a boundary
Repeat the setup, try a nearby value, or test a lower-demand or higher-cost condition. Ask when the benefit shrinks, reverses, or creates an unacceptable tradeoff. A boundary is often more useful than a single apparent optimum.
Design the comparison before pressing run
| Decision | Write before the run | Why it matters |
|---|---|---|
| Question | “If ___ changes from ___ to ___, then ___ will ___ because ___.” | Makes the mechanism and prediction falsifiable. |
| Constants | Scenario, duration, prices, staffing, quality, inventory, promotion, or other unchanged controls | Prevents a second decision from quietly explaining the result. |
| Primary measure | One outcome tied to the question, with units | Discourages selecting only the best-looking number afterward. |
| Drivers | Two or three measures that could explain the outcome | Connects the decision to a plausible operating pathway. |
| Guardrail | A limit for quality, safety, workload, service, cash, trust, waste, or another protected result | Stops one-number optimization from hiding harm. |
| Revision rule | The result that would weaken the prediction or require another test | Makes honest negative and mixed findings possible. |
If the simulator includes scenario or demand variation, record it and use matched conditions where possible. If it is deterministic, a repeat should reproduce the same result; use additional runs to test sensitivity and boundaries instead of pretending the repeated value is independent evidence.
Worked example: coffee shop morning staffing
Question: In the coffee shop simulator, will increasing morning barista staffing by one reduce queue losses enough to improve monthly profit without lowering service quality? The team holds prices, menu, drink quality, pastry plan, promotion, and starting scenario constant.
| Measure | Baseline | Changed run | Interpretation |
|---|---|---|---|
| Monthly profit | $2,180 | $2,410 | Up $230, or 10.6% |
| Average queue | 8.4 minutes | 5.9 minutes | Down 2.5 minutes |
| Labor cost | $6,900 | $7,520 | Up $620 |
| Customer rating | 76/100 | 81/100 | Guardrail held and improved |
The result supports a conditional next test, not a universal rule. Higher labor cost was offset in this modeled demand condition, but the advantage could disappear during a slower period. Repeat the matched setup, then test lower demand while monitoring profit, queue, workload, and quality. Do not claim the simulator establishes a real staffing requirement or replaces applicable scheduling, wage, safety, accessibility, or employment rules.
Printable student page
One-page controlled experiment test plan
Testable question: “If ___ changes from ___ to ___, then ___ will ___ because ___.”
Baseline anchor & constants: scenario, duration, prices, staffing, quality, inventory, promotion, or other unchanged controls
| Role in test | Measure name & unit | Baseline value | Changed value | Difference or ratio |
|---|---|---|---|---|
| Primary outcome | ||||
| Operating driver 1 | ||||
| Operating driver 2 | ||||
| Protected guardrail |
Hunt for a boundary: repeat the run, try a nearby value, or test a lower-demand or higher-cost condition
Revision rule & honest limit: what result would weaken your claim, and what can this model not prove?
Spot four misleading simulation results
1. Confusing revenue with profit
A promotion or volume surge can raise gross sales while extra labor, ingredients, overtime, commissions, or waste reduce net income.
2. Hiding damage behind a short run
Cutting staff or maintenance may improve this month’s profit while queue times, customer trust, worker fatigue, or equipment breakdowns hurt later periods.
3. Mistaking luck for strategy
A random weather surge or favorable scenario can make a weak decision look brilliant. Repeat the test or run a baseline in the same conditions.
4. Claiming universal truth from one model
A simulation is a stylized teaching tool. It does not replace real customer research, legal rules, safety standards, or professional judgment.
Classroom routes and facilitation
15-minute quick test
Pairs write one question, run baseline, change one control, and report the difference on an exit ticket.
30-minute matched pair
One student tests price while the partner tests staffing. They compare which change created better profit after accounting for tradeoffs.
50-minute full investigation
Teams design the plan, run baseline + test + boundary repeat, verify calculations, and write a one-page decision brief.
Academic integrity and simulation limits
- Use fictional scenarios and aggregate metrics; do not require personal financial, health, employment, or sensitive student data.
- Treat legal compliance, safety, accessibility, truthful advertising, privacy, fair labor, licensing, and care quality as constraints, not variables to remove for profit.
- Do not optimize ad clicks, imitate invalid traffic, or treat attention without consent and legitimate customer value as a success measure.
- Label all outputs as simulated. Do not turn a classroom comparison into financial, legal, employment, health, or safety advice.
- Assess test design, evidence, reasoning, tradeoffs, and limits—not the highest simulated profit or luckiest run.
Controlled experiment FAQ
What is a controlled experiment in a business simulation?
It is a matched comparison in which a student records a baseline, changes one main decision, holds other important settings constant, compares the same measures over the same simulated period, and repeats the test before claiming a pattern.
Why should students change only one variable at a time?
Changing one main input makes the result easier to interpret. If price, staffing, quality, and promotion all change together, the dashboard cannot show which decision produced the difference.
How many simulation runs make a useful experiment?
Three runs are a practical minimum: one baseline, one matched changed run, and one repeat or boundary check. More runs help when results vary or when students need to test whether a pattern survives different conditions.
What if a simulation does not include randomness?
A deterministic model can still support a controlled comparison, sensitivity check, and model critique. Repeats verify setup consistency, while boundary tests show where a relationship changes or stops helping.
Can a classroom simulation prove a real business decision will work?
No. A simulation is a simplified model for learning and comparison. Real decisions require current evidence, applicable rules, stakeholder input, and qualified review where legal, financial, employment, health, or safety issues matter.
Continue from test to evidence-based decision
Choose a prompt from the scenario-card bank, interpret connected outcomes, drivers, guardrails, and tradeoffs with the business simulation results-analysis guide, check calculations with the worked examples, preserve several runs in the evidence portfolio, turn the finding into a decision brief, or assess the work with the business simulation rubric. Browse every simulator and classroom tool in the complete resource index.
Use a fair test inside a strategy decision
The business strategy and decision-making simulation guide shows how to place a controlled run inside a wider decision: define alternatives and guardrails, predict a decisive difference, then recommend action with conditions.