Sensitivity Analysis in Risk Management

The concept of sensitivity analysis can often be shrouded in mystery. For many new to research, it's imagined as one specific type of analysis. However, sensitivity analysis isn't one singular test—it's about assessing how robust our findings are.💡 When we say that estimates are "robust," we mean that the results remain stable even when assumptions are changed. If results change drastically with even small changes in assumptions, it means they’re not robust. Here are key tips: 1️⃣ Measures of Central Tendency: If you use the mean in your main analysis, consider using the median in sensitivity analysis 📊 2️⃣ Contextual Definitions: For constructs without a universal definition, you might use a widely accepted definition for your primary analysis and test it with contextual modifications as part of sensitivity analysis 🧠 3️⃣ Exposure Variables: When exposure thresholds differ, try using various thresholds to define exposure. The main analysis could use the most commonly applied threshold, while sensitivity analysis explores others ⚖️ 4️⃣ Coherence with Outcomes: Looking at different outcomes measuring related aspects can strengthen your conclusions 📈 5️⃣ Outcome Specificity: If your primary outcome is less specific, explore secondary outcomes that may be more specific. For instance, looking at deaths due to smoking (more specific) vs all-cause mortality (less specific) 💀 6️⃣ Assessment Methods: If you have multiple methods for assessing the same outcome (e.g., self-reported vs. biomarker data), you can use the more accurate method as the main analysis and the less accurate one for sensitivity testing. 7️⃣ Handling Missing Values: How does your result change when you adjust for missing data? Test using different approaches like multiple imputation, listwise deletion, or inverse proportional weighting 📉 8️⃣ Model Assumptions: Test how your results hold when adjusting key model assumptions (e.g., linearity, independence) 🔧 9️⃣ Outlier Handling: Consider how sensitive your results are to extreme values. Does removing outliers or using robust methods change the outcome? 🚨 🔟 Timeframe Adjustments: For time-dependent data, check how your results change with different observation periods ⏳ 1️⃣1️⃣ Data Transformation: Examine how sensitive your findings are to data transformations (e.g., log-transformation vs Box-Cox transformation) 🔄 1️⃣2️⃣ Aggregation Level: Assess how results change when aggregating or disaggregating data (e.g., regional or demographic groupings) 🌍 1️⃣3️⃣ Uncertainty in Input Parameters: Monte Carlo simulations are a great way to test the range of possible outcomes with varying input assumptions 🎲 Bottomline: Sensitivity analysis isn’t a one-size-fits-all process—it’s context-driven. It’s not about fixing a "bad" analysis; rather, it’s about assessing how well a well-conducted analysis holds up under different assumptions and conditions💪 Please reshare ♻️ #Chisquares #VillageSchool #SensitivityAnalysis

What’s the difference between sensitivities and scenarios in FP&A? They’re often used interchangeably, but there are key differences that set them apart. In my latest collaboration with Toptal, and as Nicholas Piscani notes here, sensitivity analysis allows FP&As to show a range of potential outcomes based upon how key drivers may vary. A business’ future is inherently unpredictable, which means planning for possibilities is crucial. 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐬𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐢𝐭𝐢𝐞𝐬? Sensitivity analysis is another name for ‘what-if’ analysis. It evaluates the ramifications on outputs, flexing inputs, within a mathematical framework. An example of this might be sensitizing the pricing of software to see what the resulting customer retention rate would be. The company might expect a price hike to reduce retention, but the analysis would either confirm or dismiss whether this is true and to what extent. In finance, we commonly use sensitivity analysis to see how certain factors, when changed, influence results we care about. This allows us to seek influence or control these elements to reduce or manage risk. If increased pricing shows a heavy loss of customers, the head of customer experience may seek to add more value to reduce the blow. 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨𝐬? Scenario analysis groups various assumptions together. It allows FP&A to see what the collective impact of a decision would be on many different elements of the business. For example, a growth strategy with equity financing or debt financing, might be two scenarios that a small company would consider. Scenarios may illustrate each capitalization option’s impact on cash flow and ownership structure even if product pricing and customer experience stays the same. Why are sensitivities and scenarios important for FP&A and startups? Unlike more established business models, startups plan for the future with limited financial performance. If FP&A conducts a single analysis and presents it to an investor group, those investors know the analysis has inherent risk. But if FP&A has conducted multiple analyses, complete with sensitivities and scenarios, investors may be more encouraged by the possibilities and vetting that FP&A considered. Sensitivities and scenarios paint a fuller picture of possibilities and business outcomes and provide investors with greater confidence. 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐲𝐨𝐮: What elements in your business do you sensitize? What macroeconomic factors impact your business scenarios? #toptal #seidmanfinancial

Most “sensitivity analyses” aren’t sensitive at all. They’re just a few +/- tweaks in Excel. If you want to do it properly, here are 5 quick tips: 1. Pick the right drivers → Focus on the 3–5 variables that truly move the business (price, volume, churn, CAC). 2. Test extremes, not just margins → Push assumptions until the model breaks; that’s where you find the risks. 3. Use scenarios, not scatter → Structure downside, base, and upside cases with clear triggers. 4. Visualize impact → Tornado charts, spider plots, or even simple waterfall views make risks tangible for leaders. 5. Connect to decisions → End every sensitivity test with: “If X happens, here’s what we’ll do.” Sensitivity analysis isn’t about proving your model. It’s about showing leaders where it bends, and where it breaks. P.S. What’s the most surprising variable you’ve seen sink a “bulletproof” plan?

The Forecast That Died of Too Many What-Ifs A finance team once showed me a model with 47 scenarios. Best case. Worst case. Middle case. And then: best-worst. Worst-middle. Best-best-middle. It was like watching someone drown in their own lifeboats. Here’s the fallout: Every decision got delayed because “another scenario” needed to be tested. By the time the board approved one plan, the market had already moved on. It’s the financial version of over-training a fighter until they’re too exhausted to step in the ring. So how do you stop sensitivity analysis from becoming paralysis analysis? Use the 3-Box Rule: • One Strategic scenario (macro shifts you can’t control). • One Operational scenario (levers you can actually pull). • One Stress scenario (the “survive the punch” plan). Everything else? Noise. Because the real risk isn’t being wrong. It’s being so “thorough” you forget to decide. And here’s the twist: The companies that run fewer scenarios actually make faster moves— and that speed is what keeps them alive.