Science-Based Decision Making

Good intentions cannot build a healthy work environment. We tell leaders to be inclusive, but rarely show them how. Inclusion is a daily practice, not a feeling. Meetings where the loudest voice dominates are systems designed to exclude. My dissertation on Workplace Neurodiverse Equity used Bandura's Social Cognitive Theory to show how environments shape our capacity to thrive. Neurodiversity is the natural variation in human functioning. Everyone is part of it. Some of us just need a bit more intentional help. So, here are 10 practices to lower stress and increase support for your team: 1/ Agendas Reality: Spontaneous demands spike cortisol. Practice: Send agendas and necessary decisions 24 hours in advance. Yield: Ensures deep processing time. 2/ Brainstorming Reality: Verbal brainstorming blocks ideas. Practice: First 10 minutes are silent. Write ideas before speaking. Yield: Eliminates bias of loudest voice. 3/ Cameras Reality: Forced visual attendance drains energy. Practice: State engagement is measured by contributions, not faces. Yield: Reduces sensory overload and prevents fatigue. 4/ Cold Calls Reality: Cold calls trigger fight or flight. Practice: Give notice before asking for input. Yield: Reduces performance anxiety and restores executive function. 5/ Captions Reality: Auditory processing varies wildly. Practice: Enable live transcription on every call by default. Yield: Ensures information is captured despite barriers. 6/ Movement Reality: Movement regulates; it is not a distraction. Practice: Normalize pacing, knitting, or sketching. Yield: Increases focus and emotional regulation. 7/ Processing Time Reality: Forced participation creates anxiety. Practice: Normalize saying you need time to process. Yield: Cultivates psychological safety. 8/ Expectations Reality: Unspoken rules are invisible barriers. Practice: If an expectation matters, write it down. Yield: Eliminates ambiguity and social guessing. 9/ Visuals Reality: Auditory information is fleeting. Practice: Never just speak a point. Share screen or provide written anchor. Yield: Reinforces working memory. 10/ Transitions Reality: Back to back tasks drain executive function. Practice: End meetings at 25 or 50 minute mark. Enforce strict hard stop. Yield: Respects biological limits and allows recovery. Stop relying on good intentions. Start cultivating an environment where every mind can thrive. Just remember, we are all a bit different, stay curious, and adapt to each person. What is one neuro-inclusive practice you plan to plant in your next meeting?

Successful startup founders think like scientists. As an entrepreneur, relying on intuition and gut feelings can be tempting. But if you want to increase your chances of success, you might need to think like a scientist. I recently read a Harvard Business Review article titled "Why Entrepreneurs Should Think Like Scientists." The article highlights a study showing that startups using the scientific method generated significantly more revenue and were more likely to pivot away from unviable ideas. For the top 5%, this meant earning an additional €492,000 compared to those who didn’t apply this approach. So, how can you integrate the scientific method into your startup? 1️⃣ Test your assumptions Don’t just assume your idea will work. Test it with real customers and gather feedback. At Google for Startups, we create small pilot programs to avoid costly mistakes by learning early what works and what doesn’t. 2️⃣ Be ready to pivot Flexibility is key. If something isn’t working, be prepared to change direction. I’ve experienced this firsthand—by pivoting based on user feedback, we’ve turned potential failures into successes. 3️⃣ Use the scientific method Follow a structured process of observation, hypothesis, experimentation, and analysis. This methodical approach helps make informed decisions and drive continuous improvement. For practical application: 👉 Create an MVP Develop a basic version of your product to test your assumptions with real users. 👉 Run A/B tests Compare different versions of a feature to determine what performs best. 👉 Track your results Monitor your metrics to understand what’s working and what needs adjustment. The bottom line? Experimentation isn’t just a safety net; it’s a path to discovering what truly works for your startup. Whether you’re just starting out or looking to refine your approach, integrating the scientific method can be transformative to your startup. What’s your experience with using the scientific method in business?

Stop studying what other marketers are doing. Start importing these 6 mental models from entirely different fields. 1. Inversion (Mathematics) Origin: Mathematician Carl Jacobi's "Invert, always invert" approach Stop obsessing over how to gain customers and instead ask: "What makes customers leave?" → Real-world win: Slack transformed their retention by analysing drop-off patterns and discovering information overload was driving teams away. Using data to address the problem, they created powerful notification filtering and channel organisation tools. 2. Second-Order Thinking (Systems Theory) Origin: Systems theorist Donella Meadows' cascading effects principle Amateur marketers optimise for immediate outcomes. Professionals anticipate the domino effects. → Real-world win: Airbnb knew investing in professional photography would both increase immediate bookings (first-order effect) and establish higher visual standards across their platform as hosts competed to match the quality (second-order effect). 3. Opportunity Cost (Economics) Origin: Friedrich von Wieser's theory of alternative uses Every "yes" to a marketing activity comes with an invisible cost—what you're NOT doing instead. → Real-world win: HubSpot's decision to sunset 3,000+ underperforming content pieces allowed them to redirect resources to high-performers, resulting in traffic growth despite publishing less. 4. Falsifiability (Science) Origin: Karl Popper's scientific method If your marketing hypothesis can't be proven wrong, it's not a strategy—it's a hope. → Real-world win: Spotify's rigorous testing framework requires every new feature idea to include specific metrics that would indicate failure, ensuring objective decision-making rather than emotional attachment. 5. The Pareto Principle (Economics) Origin: Vilfredo Pareto's 80/20 distribution observation Your marketing breakthrough isn't hiding in more activity but in identifying the vital few inputs creating the majority of results. → Real-world win: Walmart's supplier strategy focused intensively on deepening relationships with the 20% of suppliers driving 80% of sales, creating growth opportunities instead of spreading attention. 6. Antifragility (Risk Theory) Origin: Nassim Taleb's concept of systems that gain from disorder Great marketing doesn't just survive disruption—it's designed to benefit from it. → Real-world win: Oreo's famous Super Bowl blackout tweet ("You can still dunk in the dark") showcased how agile teams with decision-making autonomy can capitalise on unexpected events better than competitors trapped in approval processes. The marketers who will thrive tomorrow aren't just studying what other marketers are doing—they're importing brilliance from mathematics, economics, science, and risk theory. Which of these mental models will you apply to your next marketing challenge? ♻️ Found this helpful? Repost to share with your network.  ⚡ Want more content like this? Hit follow Maya Moufarek.

Result-Based Management (RBM) in MEAL: Moving Beyond Activities to Meaningful Change In the world of Monitoring, Evaluation, Accountability, and Learning (MEAL), one question remains fundamental: “What difference are we making?” Many organizations are good at reporting what they have done — trainings conducted, communities reached, workshops held, materials distributed. However, Result-Based Management (RBM) challenges us to go beyond activities and focus on the results and changes generated. RBM is a management approach that focuses on defining clear results, measuring progress toward those results, learning from evidence, and continuously improving interventions to maximize impact. At its core, RBM answers five critical questions: 🔹 What are we trying to achieve? (Defining clear outcomes and impacts) 🔹 How will we know we are making progress? (Developing measurable indicators and targets) 🔹 What resources and actions are required? (Linking inputs, activities, outputs, outcomes, and impacts through a logical pathway) 🔹 Are our interventions producing the intended change? (Using monitoring and evaluation evidence) 🔹 How do we adapt and improve? (Embedding learning into decision-making) A strong RBM framework is often visualized through a Results Chain: Inputs → Activities → Outputs → Outcomes → Impact For example: ❌ Activity-focused thinking: “We conducted 20 entrepreneurship trainings.” ✅ Result-focused thinking: “20 entrepreneurship trainings contributed to improved business management skills, increased adoption of better practices, and enhanced income opportunities among targeted entrepreneurs.” The difference is not just in reporting — it is in the mindset. RBM encourages organizations to: ✅ Design interventions based on clearly defined problems and desired changes. ✅ Develop SMART indicators that measure progress and performance. ✅ Use evidence to inform decisions rather than relying on assumptions. ✅ Strengthen accountability to communities, donors, and stakeholders. ✅ Promote continuous learning and adaptive management. In MEAL, RBM provides the foundation for building effective: 📌 Theory of Change (ToC) 📌 Logical Frameworks (Logframes) 📌 Indicators and measurement systems 📌 Evaluation frameworks 📌 Learning agendas Ultimately, successful programs are not defined by how many activities they complete, but by the sustainable changes they create in people’s lives and communities. The future of development practice is not about doing more — it is about achieving better results, learning faster, and creating lasting impact. #MEAL #MonitoringAndEvaluation #RBM #ResultBasedManagement #TheoryOfChange #DevelopmentSector #ImpactEvaluation #ProgramManagement #Learning #Accountability

The Emperor has no Clothes: Many core Risk Management Tools are empirically proven not to work If medicine used tools with this level of empirical failure, nobody would tolerate it. Medicine relies on rigorous evidence, often from randomized trials. In corporate risk management, we still call some of them best practices. Empirical work across risk analysis, psychology, and behavioural science has shown that, for example: • Likelihood × impact scoring is not only psychometrically invalid, but mathematically and behaviourally flawed. • Inherent vs. residual risk is not only inconsistent in practice, but conceptually hypothetical and behaviourally distortive. • Risk registers document issues without influencing decisions, and they reinforce compliance rather than organizational learning. • Heat maps misrepresent risk because they treat subjective ordinal scales as if they were quantitative and falsely compress complex uncertainty into a grid. This situation stems from historical developments. Corporate risk management did not emerge from scientific research or decision-making studies; instead, it evolved from the fields of insurance, governance, and consulting. These institutional environments tend to prioritize formal clarity and accountability over rigorous empirical validation. The paradox is that the methods that measurably improve our ability to deal with uncertainty come from entirely different disciplines: • Risk engineering, which evolved in safety-critical environments, tests failure systematically and scientifically. • Decision science and psychology, which offer validated techniques such as scenario analysis, pre-mortems, base rates, and debiasing techniques. • Behavioral economics, which supports organizations in understanding and reducing systematic biases. • Forecasting research, which measures accuracy and calibrates judgment over time. These fields possess something that risk management has traditionally lacked: a culture of evidence and learning from mistakes. For risk management to stay relevant, it must build upon this cognitive foundation. This means, for example: • Replacing qualitative categories with quantified ranges, consistent with research in risk analysis and probabilistic judgment. • Embedding risk dialogue early in strategy, budgeting, and capital allocation, supported by findings from strategic decision-making and management control research. • Using validated judgment tools rather than artefacts that merely appear orderly, grounded in behavioral science and forecasting studies. • Measuring success by decisions, not documentation, in line with insights from organizational learning and governance research. Every decision is a bet on an uncertain future. Risk management should not create flawed risk maps, but rather support clearer thinking when decisions have substantial consequences. Institut für Finanzdienstleistungen Zug IFZ Lucerne University of Applied Sciences and Arts

What gets funded must be measured, and what gets measured must drive decisions. A strong Monitoring & Evaluation (M&E) Plan is the operational backbone of every high-impact public health and development program. It translates strategic intent into a measurable pathway for accountability, learning, and adaptive management. From clearly defined project objectives and SMART indicators to robust baseline data, data sources, collection methods, frequency, targets, and responsible persons, an effective M&E framework ensures that progress is not assumed—it is demonstrated with evidence. The real value of an M&E plan lies in its ability to transform raw program data into actionable intelligence for decision-makers, donors, and implementation teams. When data analysis and reporting are embedded into routine practice, organizations are better positioned to optimize performance, strengthen transparency, and maximize impact. In today’s results-driven development landscape, great programs are not judged by activity alone, but by measurable change and documented outcomes. #MonitoringAndEvaluation #MEAL #PublicHealth #GlobalHealth #ProgramManagement #DataForImpact #ResultsBasedManagement #HealthSystems #ImpactMeasurement #Leadership

Copying the summary below for 'busy people' (everyone!), but please consider reading it entirely—it'll take about 15-20 minutes. ___________ When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any findings and whether they apply to the situation in question. Such conversations should explore the internal validity of any analysis (whether it accurately answers the question) as well as its external validity (the extent to which results can be generalized from one context to another). To avoid missteps, you need to separate causation from correlation and control for confounding factors. You should examine the sample size and setting of the research and the period over which it was conducted. You must ensure that you’re measuring an outcome that really matters instead of one that is simply easy to measure. And you need to look for—or undertake—other research that might confirm or contradict the evidence. By employing a systematic approach to the collection and interpretation of information, you can more effectively reap the benefits of the ever-increasing mountain of external and internal data and make better decisions.

𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴: 𝗧𝗵𝗲 𝗠𝗼𝘀𝘁 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗘𝗹𝗲𝗺𝗲𝗻𝘁 𝗶𝗻 𝗟𝗲𝗮𝗻 - 𝗗𝗲𝗰𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗻𝗴 𝗝𝗶𝗺 𝗖𝗼𝗹𝗹𝗶𝗻𝘀 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝘁𝗵𝗲 𝗛𝗮𝗹𝗼 𝗘𝗳𝗳𝗲𝗰𝘁 Managers worldwide seek the holy grail: "How do we create sustainable profitable growth?" Countless experts have claimed to hold the answer, but none more successfully than Jim Collins. His bestsellers made him one of the world's most expensive consultants. But here's the question: Did companies following Collins' success formulas actually achieve sustainable growth? What's really needed isn't another guru's formula—it's scientific thinking. Professor Phil Rosenzweig from IMD observed that managers "lack critical thinking skills and tend to believe too easily what bestselling authors say." "Books like 'Built to Last' and 'Good to Great' don't explain the factors that drive performance, but rather capture how successful companies are portrayed" - Phil Rosenzweig 𝗜𝘀 𝗝𝗶𝗺 𝗖𝗼𝗹𝗹𝗶𝗻𝘀' 𝗧𝗵𝗲𝗼𝗿𝘆 𝗥𝗲𝗮𝗹𝗹𝘆 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰? Can his success formulas pass scientific validation? Critical thinking reveals fatal flaws: -𝗢𝘂𝘁𝗰𝗼𝗺𝗲-𝗯𝗮𝘀𝗲𝗱 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗲𝗿𝗿𝗼𝗿: Collins selected high-performing companies first, then searched for explanatory factors. This retrofits causes to results—the opposite of scientific methodology. -𝗖𝗮𝘂𝘀𝗮𝘁𝗶𝗼𝗻 𝘃𝘀 𝗰𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻 𝗰𝗼𝗻𝗳𝘂𝘀𝗶𝗼𝗻: Just because successful companies show certain characteristics doesn't mean those characteristics cause success. This is a typical causation fallacy. -𝗦𝘂𝗿𝘃𝗶𝘃𝗼𝗿𝘀𝗵𝗶𝗽 𝗯𝗶𝗮𝘀: He analyzed only companies successful at a specific time, ignoring companies with similar characteristics that failed. 𝗧𝗵𝗲 𝗛𝗮𝗹𝗼 𝗘𝗳𝗳𝗲𝗰𝘁 𝗧𝗿𝗮𝗽 Rosenzweig's "Halo Effect" penetrates this problem's core: when companies perform well, we assume they excel at everything—strategy, leadership, culture. Collins' books are based on data collected through this Halo Effect. They packaged positive descriptions of successful companies as "success secrets," but merely captured how successful companies are portrayed, not what actually drives performance. 𝗪𝗵𝗮𝘁 𝗦𝗵𝗼𝘂𝗹𝗱 𝗪𝗲 𝗗𝗼 𝗳𝗼𝗿 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗲𝗱 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲? The answer exists: decades of research on Toyota Production & Management System. Toyota research differs fundamentally: -𝗣𝗿𝗼𝗰𝗲𝘀𝘀-𝗰𝗲𝗻𝘁𝗲𝗿𝗲𝗱: Focuses on principles, not results -𝗖𝗮𝘂𝘀𝗮𝘁𝗶𝗼𝗻 𝘃𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Identifies how practices lead to outcomes -𝗥𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Other companies can apply and achieve similar results The most important thing in Lean isn't tools, but 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴. Form hypotheses, validate through experiments, clarify causal relationships. This is genuine improvement without falling into the Halo Effect trap. #Phil_Rosenzweig #Halo_Effect #Scientific_Thinking #Critical_Thinking #Causation

𝐇𝐨𝐰 𝐝𝐨 𝐰𝐞 𝐤𝐧𝐨𝐰 𝐢𝐟 𝐚 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐢𝐧𝐭𝐞𝐫𝐯𝐞𝐧𝐭𝐢𝐨𝐧 𝐢𝐬 𝐭𝐫𝐮𝐥𝐲 𝐞𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞, 𝐞𝐪𝐮𝐢𝐭𝐚𝐛𝐥𝐞, 𝐚𝐧𝐝 𝐰𝐨𝐫𝐭𝐡 𝐭𝐡𝐞 𝐢𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭? As the shift toward evidence-based decision-making accelerates, we need more than good intentions. We need evidence, structure, and reliable data to design, monitor, and evaluate programs that create sustainable impact. This resource on Planning, Monitoring and Evaluation (PM&E): Methods and Tools offers practical approaches used globally to strengthen accountability and reduce poverty and inequality. It introduces proven methods such as cost-benefit analysis, causality frameworks, benchmarking, process and impact evaluations, all backed by real-world case studies. These tools help ensure that projects are not only well-designed but also deliver meaningful results. This document is especially valuable for: ✅ Civil society leaders designing impactful projects ✅ Policy makers & donors demanding accountability ✅ M&E professionals refining their evaluation toolbox ✅ Students & researchers deepening their knowledge of results-based management #MonitoringAndEvaluation #PME #ResultsBasedManagement #Accountability #EvidenceBasedPolicy #CivilSociety #ImpactEvaluation #DevelopmentTools

Monitoring and evaluation provide the foundation for understanding performance, learning from experience and improving decision-making across programmes and policies. The document explains core M&E concepts by clarifying key principles, defining essential terminology and describing how monitoring and evaluation functions support accountability, learning and results-based management. This guide brings together the following essential elements: – Fundamental M&E concepts, including monitoring, evaluation, results chains, indicators, baselines, targets, assumptions and risks – Core principles guiding effective M&E practice such as relevance, credibility, utility, participation, transparency and ethical conduct – Distinctions between monitoring and evaluation, and how each contributes differently to performance tracking, learning and accountability – Common M&E frameworks and tools, including logical frameworks, theories of change, results frameworks and performance measurement plans – Indicator types and measurement considerations, covering input, output, outcome and impact indicators, as well as qualitative and quantitative measures – Data-collection and data-management approaches, including routine monitoring systems, surveys, assessments and administrative data – Roles and responsibilities of programme teams, M&E staff and stakeholders in implementing and using M&E systems – Use of M&E findings for adaptive management, reporting, organisational learning and strategic decision-making The document provides a coherent reference that supports practitioners in understanding and applying M&E concepts, principles and tools in a consistent manner, strengthening evidence-based management and continuous improvement across interventions.