Comparing Subjective and Empirical Data in Risk Analysis

Qualitative risk assessment and quantitative risk assessment are two approaches used to evaluate and measure risks, but they differ in several key aspects: 1. Nature of the data: Qualitative risk assessment relies on subjective, descriptive data and expert judgment to evaluate the relative significance of risks. It involves the assessment of risk likelihood and impact using qualitative scales or categories such as low, medium, or high. On the other hand, quantitative risk assessment involves the use of quantitative data, numbers, and mathematical models to measure risks. It uses tools like probability distributions, statistical data, and mathematical formulas to assess the probability and potential consequences of risks. 2. Precision: Qualitative risk assessment leads to relatively subjective results as it lacks precise numerical values. It focuses on subjective judgments, opinions, and experience-based assessments, which can introduce subjectivity and bias into the risk evaluation process. Quantitative risk assessment, on the other hand, aims to provide more precise and objective results by utilizing numerical data and calculations. It provides a more quantifiable understanding of risk likelihoods, impacts, and potential outcomes. 3. Complexity: Qualitative risk assessment is simpler and less time-consuming compared to quantitative risk assessment. It generally requires less data gathering and analysis, making it suitable for quick risk assessments or when limited resources are available. Quantitative risk assessment, on the other hand, is more complex and resource-intensive. It requires more data collection, statistical analysis, and mathematical modeling to estimate risks accurately. 4. Communication: Qualitative risk assessment provides a more accessible and easily understandable way to communicate risk information to stakeholders who may not have expertise in quantitative analysis. Results are often presented in the form of qualitative descriptors, visual representations, or risk matrices. Quantitative risk assessment, however, may be more challenging to communicate effectively to non-experts due to its reliance on numerical values, statistical concepts, and mathematical formulas. 5. Application: Qualitative risk assessment is often used in the early stages of risk management or when detailed data is lacking. It can help identify and prioritize risks, provide a general understanding of the risk landscape, and inform decision-making. Quantitative risk assessment is more commonly applied in situations where accurate and precise risk estimates are required, such as in complex projects, financial analysis, or regulatory compliance assessments. It is important to note that both qualitative and quantitative risk assessments have their own strengths and limitations, and they can be complementary approaches.

When and Why to Use Qualitative vs. Quantitative Risk Analysis? Risk analysis plays a crucial role in decision-making across industries. The two main approaches—qualitative and quantitative—serve different purposes and are applied based on the nature and complexity of the risks involved. 1. Qualitative Risk Analysis When to Use: • When quick decision-making is required. • When data availability is limited or insufficient for numerical analysis. • For initial risk assessments to prioritize risks. • When subjective judgment and experience play a major role. Why Use It: • Provides a broad overview of potential risks. • Helps in identifying high-priority risks without complex calculations. • Involves stakeholders easily through risk workshops and brainstorming. Common Tools: • Risk matrices (e.g., impact vs. likelihood). • Expert judgment and experience. • Risk categorization and ranking. 2. Quantitative Risk Analysis When to Use: • When numerical data is available for precise calculations. • For financial impact assessments and scenario analysis. • When dealing with highly complex risks that require measurable data. • When regulatory compliance demands data-backed decisions. Why Use It: • Enables data-driven decision-making with statistical confidence. • Provides a clear monetary impact of risks. • Useful in planning mitigation strategies with resource allocation. Common Tools: • Monte Carlo simulations. • Value at Risk (VaR). • Sensitivity analysis and statistical modeling. Which One is Better? Both methods complement each other and should be used together for a comprehensive risk management framework. Qualitative analysis helps in the initial screening of risks, while quantitative analysis provides deeper insights into the impact and likelihood of risks. #RiskManagement #QualitativeRisk #QuantitativeRisk #RiskAssessment #DecisionMaking #BusinessRisk #StrategicPlanning #RiskAnalysis #RiskMitigationq

Risk Assessment. Risk assessment is “The process of quantifying the probability of a risk occurring and its likely impact on the project”. It is often undertaken, at least initially, on a qualitative basis by which I mean the use of a subjective method of assessment rather than a numerical or stochastic (probablistic) method. Such methods seek to assess risk to determine severity or exposure, recording the results in a probability and impact grid or ‘risk assessment matrix'. The infographic provides one example which usefully visually communicates the assessment to the project team and interested parties. Probability may be assessed using labels such as: Rare, unlikely, possible, likely and almost certain; whilst impact considered using labels: Insignificant, minor, medium, major and severe. Each label is assigned a ‘scale value’ or score with the values chosen to align with the risk appetite of the project and sponsoring organisation. The product of the scale values (i.e. probability x impact) resulting in a ranking index for each risk. Thresholds should be established early in the life cycle of the project for risk acceptance and risk escalation to aid decision-making and establish effetive governance principles. Risk assessment matrices are useful in the initial assessment of risk, providing a quick prioritisation of the project’s risk environment. It does not, however, give a full analysis of risk exposure that would be accomplished by quantitative risk analysis methods. Quantitative risk analysis may be defined as: “The estimation of numerical values of the probability and impact of risks on a project usually using actual or estimated values, known relationships between values, modelling, arithmetical and/or statistical techniques”. Quantitative methods assign a numerical value (e.g. 60%) to the probability of the risk occurring, where possible based on a verifiable data source. Impact is considered by means of more than one deterministic value (using at least 3-point estimation techniques) applying a distribution (uniform, normal or skewed) across the impact values. Quantitative risk methods provide a means of understanding how risk and uncertainty affect a project’s objectives and a view of its full risk exposure. It can also provide an assessment of the probability of achieving the planned schedule and cost estimate as well as a range of possible out-turns, helping to inform the provision of contingency reserves and time buffers. #projectmanagement #businesschange #roadmap

The “subjectivity beast” in risk analysis: Are statistical models better than expert opinions?   This post is a matter of the heart. I have heard and read so many (misleading) statements about the superiority of “objective” (i.e., statistical) over “subjective” (i.e., expert opinion-based) risk analysis.   It is a complex topic that deserves much more than a simple post. I can’t cover the complexity and nuances surrounding it. See it as a starting point for a hopefully great discussion.   It is true that, for some risks, data-driven risk analysis and even simple quantitative algorithms regularly outperform experts, as clearly shown by the evidence. There are many reasons for this, like biases at play, an environment where experiences lead to learning, too little experience with certain risks, and many more.   It is not true that statistical models mean “objective risk analysis.” Many decisions remain highly subjective, such as the choice of the statistical model, the choice of the sample, and assumptions about the causality embedded in the model. It is tempting to confuse objectivity with “quantitative” risk analysis and subjectivity with “qualitative risk analysis.” I'm afraid that's not right. Here is why:   Pure quantitative statistical models can also entirely rely on subjective probability and impact distributions assessed by experts. For example, I can conduct a Monte Carlo simulation based on a triangular distribution in which experts guess the worst, best, and most likely scenarios.   Also, statistical analysis results require human interpretation, which might be biased. A statistical model fails to ensure the analysis problems are correctly framed (e.g., risk scenarios that only cover short-term impacts). Statistical analysis starts and ends with subjective decisions. Specifically, in the case of rare risks, expert opinion may outperform statistical analysis just because no data exists. Remember that probability theory cannot be applied to assessing single-event risks that have yet to occur.   Experts may hint at the wrong model assumptions, have some data, and have an educated opinion (the combination may be better than just relying on data). Experts may use scenario analysis to reveal wrongly framed risks. Experts may decompose complex risks by using event tree analysis. Experts may adjust the results of data-driven analysis.   So what does that mean? Two things: First, there is no such thing as objective risk analysis, even if your risk management is fully “quantitative.” It may even lead to the paradox that quantitative risk analysis is more biased as it is believed to be objective. Second, for some risks, the dominant strategy is to rely on expert opinion. For a good reason: Experts may outperform statistical analysis in assessing rare (but detrimental) risks. Institut für Finanzdienstleistungen Zug IFZ Lucerne University of Applied Sciences and Arts #ifzriskmanagement

𝗤𝘂𝗮𝗹𝗶𝘁𝗮𝘁𝗶𝘃𝗲 & 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗥𝗶𝘀𝗸 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 Effective risk management begins with accurate and structured risk assessment. Two fundamental approaches used across industries are Qualitative Risk Assessment and Quantitative Risk Assessment. While both aim to identify, evaluate, and prioritize risks, they differ in methodology, data requirements, and depth of analysis. Qualitative Risk Assessment relies on professional judgment, experience, and descriptive scales (e.g., low, medium, high) to estimate the likelihood and consequences of potential hazards. It offers a fast, intuitive, and accessible way to screen and manage risks, especially when numerical data is limited. In contrast, Quantitative Risk Assessment (QRA) involves numerical modeling, statistical analysis, and measurable data to calculate risk values. It provides a more detailed, objective view and is commonly used in high-risk environments where precise risk estimates are essential. Understanding both methods and knowing when and how to apply them is crucial for developing a balanced, reliable, and effective risk management strategy. This document explores the principles, processes, advantages, and applications of both qualitative and quantitative approaches to help safety professionals make informed decisions and enhance workplace safety. 𝗘𝗺𝗽𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝗦𝗮𝗳𝗲𝘁𝘆 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀: 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲, 𝗚𝗿𝗼𝘄𝘁𝗵, 𝗮𝗻𝗱 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 / 𝗧𝗵𝗲 𝗦𝗮𝗳𝗲𝘁𝘆 𝗟𝗲𝗮𝗱𝗲𝗿’𝘀 𝗠𝗶𝗻𝗱𝘀𝗲𝘁 𝗪𝗵𝗮𝘁𝘀𝗔𝗽𝗽: https://lnkd.in/dMKWKNbp 𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸: https://lnkd.in/dPNUqCUJ 𝗛𝗦𝗘 𝗝𝗼𝗯𝘀 𝘀𝗲𝗮𝗿𝗰𝗵: https://lnkd.in/dRscT4Zb

Effective risk analysis and management are fundamental to project success. Irrespective of the size or scale of your project, delivering it on time and within budget (not to mention preserving stakeholder confidence) is impossible if you don't take the time to identify, analyze, categorize, prioritize, and gauge the impact of external risks before work commences. Two well-established methodologies dominate risk analysis: qualitative and quantitative. Yet, despite their universality, a surprising number of people within the project management bubble struggle to understand how best to deploy these methodologies. * Qualitative Risk Analysis is Subjective The most obvious difference between qualitative and quantitative risk analysis is their approach to the process. Qualitative risk analysis tends to be more subjective. It focuses on identifying risks to measure both the likelihood of a specific risk event occurring during the project life cycle and the impact it will have on the overall schedule should it hit. The goal is to determine severity. Results are then recorded in a risk assessment matrix (or any other form of an intuitive graphical report) in order to communicate outstanding hazards to stakeholders. * Quantitative Risk Analysis is Objective Quantitative risk analysis uses verifiable data to analyze the effects of risk in terms of cost overruns, scope creep, resource consumption, and schedule delays. Uncertainty and Identified Risks Uncertainty and identified risks are two distinct factors that influence the variability of results for schedule and cost. These are the factors we're trying to quantify. - Uncertainty is background variability, distinct from variation caused by identifiable risks. It's caused by at least 3 common factors in projects: 1. The inherent variability of the work not caused by identified risks 2. Estimating error or error of prediction 3. Bias in estimation or prediction Uncertainty is always present at some level of impact, so its probability is 100%. Since its source is unknown, uncertainty can't be mitigated during the time of one project. - Identified risks are root causes of variability that can be measured and moderated or mitigated. There are two types of these risks: 1. Project-specific risks 2. Systemic risks Quantifying an identified risk using Risk Drivers represents the probability that the risk will occur on this project and the impact the risk has on the duration of the activities it affects if it occurs.

Really interesting pod with Prof Terje Aven discussing risk science. Highly recommend the listen. Some extracts: ·        “there is often a difference between experts' risk judgments and people's risk perception. But this difference can be explained also by the fact that people's judgments could incorporate aspects of uncertainty not covered by the experts' risk perspectives” ·    “Experts often restrict their assessment to probabilities, expected values and historical data and do not cover all relevant aspects of risk” ·     “restricting risk to probabilities, expected values and historical data is problematic” ·    Moreover “uncertainty should be a main component of risk, and hence people's risk judgments or perceptions could reflect risk better than the expert's judgment in some cases” ·    On whether “Risk is a social construction”, for Aven this isn’t controversial if “risk is interpreted as the risk assessment results, the risk characterization results” ·    “The risk measurements etc. … is not objective. For example, when saying that the likelihood for this event to occur, leading to severe consequences … And the knowledge supporting this likelihood judgment is strong. This is a judgment made by the risk analyst. It is subjective or intersubjective” ·    Aven argues that “social sciences have deeply altered our understanding of what risk means, from something real and physical, if hard to measure, and accessible only to experts, to something constructed out of history and experienced by experts and laypeople alike” ·    “Risk in this sense is culturally embedded and has texture and meaning that vary from one social grouping to another. Trying to assess risk is therefore necessarily a social and political exercise, even when the methods employed are the seemingly technical routines of quantitative risk assessment” ·    Hence, “risk assessments and related characterizations are social constructions, but that is not the same as saying that risk as a concept is a social construction” ·    Some see risk as an event, and from that perspective “risk is not to be seen as a social construction at all. It is objective to some extent” ·    “But when we come to the measurement and characterization of the magnitude of this risk, we perform a risk assessment. The result is to be seen as a social construction” ·    For quantification, “We assess risk, but risk does not need to be quantified to be informative” ·    But quantification can be useful. E.g. in aviation “we can conclude that it is safe to fly as the probability of undesirable consequences is low and the supporting knowledge strong” ·    And regarding the strength of knowledge and degree of uncertainty surrounding judgements, “We need to conclude that the data are relevant and that the evidence supporting the probability judgments is strong” ·    “The key message, qualitative judgments always accompany the quantification” https://lnkd.in/gEagHnwC

🔔 Monte Carlo Simulation Challenges. Is Quantitative Risk Analysis objective? 🔔 "Quantitative Risk Analysis is Objective!". This myth is often promoted by companies selling Monte Carlo Simulation software, training, or consulting services. For example, one vendor’s website states: ‘Qualitative Risk Analysis is Subjective. Quantitative Risk Analysis is Objective.’ 🚫 This is a common misconception. A Monte Carlo Simulation doesn’t make data ‘objective’, it just processes it mathematically. 🔔 Fact: Quantitative Risk Analysis based on subjective inputs is still subjective. Many people assume: ▪️ Qualitative Risk Analysis = subjective, because it relies on expert judgment (e.g., “high”, “medium”, “low”). ▪️ Quantitative Risk Analysis = objective, because it uses numbers, probability distributions, and Monte Carlo simulations. 💡The fallacy lies in believing that numbers automatically create objectivity.   ⚠️ Why This Is Wrong Quantitative analysis is only as objective as its inputs - the data and assumptions behind it. If probabilities, impacts, correlations, or mitigation effects are based on subjective judgments, then the result, no matter how mathematically elegant, remains subjective in disguise. What is even worse, Project Delivery Monte Carlo Simulation Models are often based on speculative, not even subjective, inputs. The lack of well-evaluated three-point estimations forces risk consultants to guestimate uncertainties and probability distributions.    ✅ The Correct View ▪️Qualitative Risk Analysis: Subjective and openly acknowledged as such. It uses descriptive scales to prioritise risks. ▪️Quantitative Risk Analysis: Can be objective only if based on reliable empirical or statistically validated data. But if based on subjective estimates (expert opinion, workshop guesses, or analogies), and convenient assumptions (unlimited resources), it remains subjective. 💡 Numbers don’t equal objectivity - evidence does. 💡 Quantitative methods can amplify subjectivity if users assume the results are “scientific” without validating the inputs. The danger is “false precision”: producing charts and probability curves that look objective but are built on personal judgments. Ask yourself: ‘What is worse, acknowledged subjective analysis or subjective analysis perceived as objective?’ 💡 It’s not uncertainty that hurts projects. It’s the illusion of certainty. #PredAptivePM #RiskAnalysis #MonteCarloSimularion #QRA