{"id":326180,"date":"2026-09-27T04:10:00","date_gmt":"2026-09-27T01:10:00","guid":{"rendered":"https:\/\/ceotudent.com\/base-rates-outside-view-underused-fix-bad-predictions"},"modified":"2026-09-27T04:10:00","modified_gmt":"2026-09-27T01:10:00","slug":"base-rates-outside-view-underused-fix-bad-predictions","status":"publish","type":"post","link":"https:\/\/ceotudent.com\/en\/base-rates-outside-view-underused-fix-bad-predictions","title":{"rendered":"Base Rates and the Outside View: The Most Underused Fix for Bad Predictions"},"content":{"rendered":"<p><strong>TL;DR.<\/strong> When we predict how long a project will take, whether a venture will survive or how a decision will play out, we naturally build a story from the details of our own case. Daniel Kahneman and Amos Tversky called this the <strong>inside view<\/strong>. The fix is the <strong>outside view<\/strong>: before looking at your case, ask how cases like it usually turn out, and start from that <strong>base rate<\/strong>. The gap it closes is large. Students who predicted their thesis would take 33.9 days took 55.5 days on average, and only 29.7% finished by their own best estimate. Across 1,603 large projects, actual costs averaged 1.39 times the estimate. Only about half of new US business establishments are still operating five years after opening. The good news is that the correction is cheap and trainable: in geopolitical forecasting tournaments, a training module of less than an hour that emphasized comparison classes and base rates improved accuracy by 6 to 12 percent, and base rates were the principle most associated with better forecasts. This guide gives you a library of verified base rates, the five-step correction in the form of a worksheet, and worked examples.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/ceotudent.com\/en\/base-rates-outside-view-underused-fix-bad-predictions\/#Inside-view-vs-outside-view\" >Inside view vs outside view<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/ceotudent.com\/en\/base-rates-outside-view-underused-fix-bad-predictions\/#Why-base-rates-get-ignored\" >Why base rates get ignored<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/ceotudent.com\/en\/base-rates-outside-view-underused-fix-bad-predictions\/#A-library-of-verified-base-rates\" >A library of verified base rates<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/ceotudent.com\/en\/base-rates-outside-view-underused-fix-bad-predictions\/#The-five-step-correction\" >The five-step correction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/ceotudent.com\/en\/base-rates-outside-view-underused-fix-bad-predictions\/#The-outside-view-worksheet-with-worked-examples\" >The outside-view worksheet, with worked examples<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/ceotudent.com\/en\/base-rates-outside-view-underused-fix-bad-predictions\/#When-the-outside-view-is-hard-to-use\" >When the outside view is hard to use<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/ceotudent.com\/en\/base-rates-outside-view-underused-fix-bad-predictions\/#Why-it-matters-more-in-the-AI-era\" >Why it matters more in the AI era<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/ceotudent.com\/en\/base-rates-outside-view-underused-fix-bad-predictions\/#The-CEO-and-the-student-in-your-predictions\" >The CEO and the student in your predictions<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/ceotudent.com\/en\/base-rates-outside-view-underused-fix-bad-predictions\/#Frequently-asked-questions\" >Frequently asked questions<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/ceotudent.com\/en\/base-rates-outside-view-underused-fix-bad-predictions\/#Sources\" >Sources<\/a><\/li><\/ul><\/nav><\/div>\n<h2 id=\"inside-view-vs-outside-view\"><span class=\"ez-toc-section\" id=\"Inside-view-vs-outside-view\"><\/span>Inside view vs outside view<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Kahneman and Lovallo defined the two views in a 1993 paper in Management Science. An inside-view forecast &ldquo;draws on knowledge of the specifics of the case, the details of the plan that exists, some ideas about likely obstacles and how they might be overcome.&rdquo; The outside view, by contrast, &ldquo;is essentially statistical and comparative, and involves no attempt to divine future history at any level of detail.&rdquo;<\/p>\n<p>Kahneman&rsquo;s own favorite illustration comes from 1976, when he was part of a team writing a curriculum on judgment and decision making for Israeli high schools. After about a year of work, the team members each estimated how long the project would take to finish: the estimates ranged from 18 to 30 months. Then one member, an expert in curriculum development, was asked how similar teams had done. His answer, as reported in the 1993 paper: about 40% of them eventually gave up, and of the rest, he could not think of any that finished in less than seven years or took more than ten. He also judged their team to be slightly below average. In Lovallo and Kahneman&rsquo;s later retelling in Harvard Business Review (2003), as quoted by Flyvbjerg, the curriculum was finished eight years later and was rarely used.<\/p>\n<p>Nobody on the team lacked intelligence or information. The information that would have changed the forecast was available in the room. It simply was not consulted, because the natural way to think about a case is from the inside.<\/p>\n<h2 id=\"why-base-rates-get-ignored\"><span class=\"ez-toc-section\" id=\"Why-base-rates-get-ignored\"><\/span>Why base rates get ignored<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>In a 1977 technical report that became the foundation of this field, Kahneman and Tversky distinguished two kinds of information. <strong>Singular information<\/strong> is evidence about the case at hand. <strong>Distributional information<\/strong>, or base-rate data, is knowledge about how outcomes are distributed across similar cases. Their verdict: &ldquo;The prevalent tendency to underweight, or ignore, distributional information is perhaps the major error of intuitive prediction.&rdquo; The same report coined the term &ldquo;planning fallacy&rdquo; for the resulting habit of underestimating how long and how much things take.<\/p>\n<p>The classic laboratory demonstration is the cab problem, analyzed by Maya Bar-Hillel in 1980. In a city, 85% of cabs are Blue and 15% are Green. A witness says the cab in a hit-and-run was Green, and the witness is correct 80% of the time. What is the probability the cab was Green? The correct answer, combining the base rate with the witness&rsquo;s reliability, is about 41%. The median answer in Bar-Hillel&rsquo;s study was 80%; 36% of participants simply gave the witness&rsquo;s reliability, and only about 10% came close to 41%. The vivid, specific evidence (a witness) crowded out the dull, general evidence (how many cabs of each color exist).<\/p>\n<p>Bar-Hillel&rsquo;s explanation applies well beyond cabs: people rank information by how relevant it feels and let the most relevant-feeling information dominate. Details about your own project always feel more relevant than statistics about other people&rsquo;s projects. That is precisely why the statistics get ignored, and why they are so valuable.<\/p>\n<h2 id=\"a-library-of-verified-base-rates\"><span class=\"ez-toc-section\" id=\"A-library-of-verified-base-rates\"><\/span>A library of verified base rates<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The first practical step is to know that base rates exist for many decisions you face. The table below collects base rates from published, verifiable sources.<\/p>\n<p><strong>Table 1. Verified base rates for common predictions<\/strong><\/p>\n<table>\n<thead>\n<tr>\n<th>Prediction<\/th>\n<th>Base rate<\/th>\n<th>Source and scope<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Will I finish my project by my best estimate?<\/td>\n<td>29.7% finished by their own best estimate; average 55.5 days actual vs 33.9 predicted<\/td>\n<td>Buehler, Griffin and Ross (1994), honors thesis students, Study 1 (33 students)<\/td>\n<\/tr>\n<tr>\n<td>Will I finish even by my worst-case estimate?<\/td>\n<td>48.7% finished by their own &ldquo;everything goes as poorly as possible&rdquo; date<\/td>\n<td>Buehler, Griffin and Ross (1994)<\/td>\n<\/tr>\n<tr>\n<td>Will a large project stay on budget?<\/td>\n<td>Actual cost averaged 1.39 times the estimate (1,603 projects); roads 1.24, rail 1.40, buildings 1.36, dams 1.96<\/td>\n<td>Flyvbjerg (2021), Project Management Journal, cost measured from final investment decision, real terms<\/td>\n<\/tr>\n<tr>\n<td>Will a large project deliver its promised benefits?<\/td>\n<td>Actual benefits averaged 0.94 times the forecast (786 projects); rail 0.66<\/td>\n<td>Flyvbjerg (2021)<\/td>\n<\/tr>\n<tr>\n<td>Will a new business location survive one year?<\/td>\n<td>77.9% (establishments opened in the year to March 2024)<\/td>\n<td>US Bureau of Labor Statistics, Business Employment Dynamics<\/td>\n<\/tr>\n<tr>\n<td>Will it survive five years?<\/td>\n<td>51.4% (establishments opened in the year to March 2020)<\/td>\n<td>BLS, Business Employment Dynamics<\/td>\n<\/tr>\n<tr>\n<td>Will it survive ten years?<\/td>\n<td>34.7% (establishments opened in the year to March 2015)<\/td>\n<td>BLS, Business Employment Dynamics<\/td>\n<\/tr>\n<tr>\n<td>Will a drug entering clinical trials be approved?<\/td>\n<td>13.8% overall; 3.4% in oncology; 33.4% for vaccines<\/td>\n<td>Wong, Siah and Lo (2019), Biostatistics, trials from 2000 to 2015<\/td>\n<\/tr>\n<tr>\n<td>How much should a UK public building budget be adjusted for optimism bias?<\/td>\n<td>Up to 24% (standard buildings) and 51% (non-standard buildings) on capital expenditure<\/td>\n<td>HM Treasury, Supplementary Green Book Guidance on Optimism Bias, based on Mott MacDonald (2002)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>A few cautions make these numbers more useful, not less:<\/p>\n<ul>\n<li><strong>Scope matters.<\/strong> The BLS figures count establishments, meaning single business locations, not startups; a new branch of an existing chain counts as an opening. They are a reasonable outside view for &ldquo;will this new location still be open?&rdquo;, not for &ldquo;will this venture-backed startup succeed?&rdquo;<\/li>\n<li><strong>Small samples are small.<\/strong> The thesis study had 33 students. Its result has been replicated in spirit many times, but the exact 29.7% is one study&rsquo;s figure.<\/li>\n<li><strong>Use overall figures when the details are disputed.<\/strong> Wong, Siah and Lo published a correction to some of their phase-by-phase tables, which is why only the overall and therapeutic-area figures are used here.<\/li>\n<\/ul>\n<h2 id=\"the-five-step-correction\"><span class=\"ez-toc-section\" id=\"The-five-step-correction\"><\/span>The five-step correction<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Kahneman and Tversky&rsquo;s 1977 report set out a corrective procedure in five steps. Bent Flyvbjerg later adapted it for large projects as <strong>reference class forecasting<\/strong>, and in 2004 the UK Department for Transport and HM Treasury adopted it for appraising large transport projects. Here is the procedure, adapted for individual decisions.<\/p>\n<ol>\n<li><strong>Select a reference class.<\/strong> What group of past cases is your case a member of? Flyvbjerg&rsquo;s rule: broad enough to be statistically meaningful, narrow enough to be truly comparable.<\/li>\n<li><strong>Find the distribution for that class.<\/strong> What is the average outcome, and how widely do outcomes vary?<\/li>\n<li><strong>Make your intuitive estimate.<\/strong> Now, and only now, use everything you know about your specific case.<\/li>\n<li><strong>Assess predictability.<\/strong> How well do estimates like yours actually predict outcomes? Kahneman and Tversky express this as a correlation between 0 (your case information predicts nothing) and 1 (it predicts perfectly).<\/li>\n<li><strong>Correct toward the base rate.<\/strong> Move from the base rate toward your intuitive estimate only in proportion to that predictability.<\/li>\n<\/ol>\n<p>In formula form, step 5 is:<\/p>\n<p><strong>Corrected estimate = base rate + predictability x (intuitive estimate &#8211; base rate)<\/strong><\/p>\n<p>Kahneman and Tversky&rsquo;s own example: an editor&rsquo;s intuition says a book will sell 12,000 copies; books in its category average 4,000; the editor&rsquo;s judgment has a predictability of about 0.6. The corrected forecast is 4,000 + 0.6 x (12,000 &#8211; 4,000) = 8,800 copies.<\/p>\n<p>The formula has two useful extremes. If your case information predicts nothing (predictability 0), use the base rate. If it predicts perfectly (predictability 1), use your intuition. Real life is almost always in between, and most people behave as if they were at 1.<\/p>\n<h2 id=\"the-outside-view-worksheet-with-worked-examples\"><span class=\"ez-toc-section\" id=\"The-outside-view-worksheet-with-worked-examples\"><\/span>The outside-view worksheet, with worked examples<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The worksheet below applies the five steps to four common decisions. The base rates come from Table 1; the intuitive estimates and predictability values are illustrative choices you would replace with your own.<\/p>\n<p><strong>Table 2. Outside-view worksheet: four worked examples (CEOtudent editorial framework)<\/strong><\/p>\n<table>\n<thead>\n<tr>\n<th>Decision<\/th>\n<th>Reference class and base rate<\/th>\n<th>Intuitive estimate<\/th>\n<th>Predictability (illustrative)<\/th>\n<th>Corrected estimate<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Book sales (Kahneman and Tversky&rsquo;s example)<\/td>\n<td>Category average: 4,000 copies<\/td>\n<td>12,000 copies<\/td>\n<td>0.6<\/td>\n<td>8,800 copies<\/td>\n<\/tr>\n<tr>\n<td>Will my new shop still be open in five years?<\/td>\n<td>New US business establishments: 51.4% survive five years<\/td>\n<td>90%<\/td>\n<td>0.3<\/td>\n<td>63.0%<\/td>\n<\/tr>\n<tr>\n<td>What should I budget for a building project I estimate at $200,000?<\/td>\n<td>Large building projects: actual cost 1.36 times the estimate, so outside-view budget $272,000<\/td>\n<td>$200,000<\/td>\n<td>0.5<\/td>\n<td>$236,000<\/td>\n<\/tr>\n<tr>\n<td>How long will my project take if I think 30 days?<\/td>\n<td>Thesis students took 55.5 days vs 33.9 predicted, a ratio of 1.64, so outside-view duration about 49 days<\/td>\n<td>30 days<\/td>\n<td>0.3<\/td>\n<td>About 43 days<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><em>Table 2 is the CEOtudent editorial framework. The base rates are the published figures in Table 1; the intuitive estimates and predictability values are illustrative, and each corrected estimate applies the formula above. Arithmetic was checked by script. For the budget and duration rows, the base rate is expressed as an outside-view figure for this case (your estimate times the reference-class ratio) before correcting.<\/em><\/p>\n<p>How to choose the predictability value is the hardest part, so here is a practical guide:<\/p>\n<ul>\n<li><strong>Around 0 to 0.2:<\/strong> you have no track record in this kind of decision, or the outcome depends mostly on factors outside your control (a new market, a first venture).<\/li>\n<li><strong>Around 0.3 to 0.5:<\/strong> you have some relevant experience and your case has real, measurable differences from the average.<\/li>\n<li><strong>Around 0.6 and above:<\/strong> you have a documented track record of similar estimates compared with outcomes. Kahneman and Tversky suggest estimating predictability from exactly such records, which is one reason <a href=\"https:\/\/ceotudent.com\/en\/decision-journal-template-protocol-improving-judgment\/\">a decision journal<\/a> pays off.<\/li>\n<\/ul>\n<p>If you cannot justify a number, choose a lower one. The whole point of the planning fallacy is that people overrate how much their case information tells them.<\/p>\n<h2 id=\"when-the-outside-view-is-hard-to-use\"><span class=\"ez-toc-section\" id=\"When-the-outside-view-is-hard-to-use\"><\/span>When the outside view is hard to use<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The method has real limits, and knowing them keeps it honest.<\/p>\n<ul>\n<li><strong>&ldquo;But my case is unique.&rdquo;<\/strong> Kahneman and Lovallo observed that decision makers &ldquo;have a strong tendency to consider problems as unique.&rdquo; Almost nothing is. Even a genuinely new product is a member of the class &ldquo;new products&rdquo;, &ldquo;projects built by this team&rdquo; and &ldquo;projects of this size&rdquo;. Use more than one reference class and see whether they agree.<\/li>\n<li><strong>The class is too broad or too narrow.<\/strong> Too broad and it says nothing about your case; too narrow and it contains three examples. In a worked example from Chang and colleagues&rsquo; paper on the Good Judgment Project&rsquo;s training, the base rate for a country possessing weapons of mass destruction was about 13% across all countries and about 17% when narrowed to the Middle East. When nearby classes give similar numbers, you can trust the estimate more.<\/li>\n<li><strong>The world changes.<\/strong> Base rates describe the past. If a real structural change has occurred (a new technology, a new rule), adjust, but name the change explicitly and ask whether you would have accepted that argument from someone else.<\/li>\n<li><strong>Averages hide tails.<\/strong> The outside view also tells you about variability. Flyvbjerg&rsquo;s version expands the uncertainty range toward the class&rsquo;s range, not only the midpoint. A budget that is right on average but has no reserve for the bad tail is still a bad budget.<\/li>\n<\/ul>\n<h2 id=\"why-it-matters-more-in-the-ai-era\"><span class=\"ez-toc-section\" id=\"Why-it-matters-more-in-the-AI-era\"><\/span>Why it matters more in the AI era<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Generative AI tools make the inside view cheaper than ever. Ask for a plan and you get a detailed, confident scenario for your case in seconds. That is useful, but it is exactly the kind of information Kahneman and Tversky warned tends to dominate judgment. The outside view is the counterweight, and it is easy to build into how you use these tools: ask first for the reference class and how such cases usually turn out, and only then for the plan. Then check any base rate the tool provides against a primary source, because a fabricated statistic is worse than none.<\/p>\n<p>For the wider set of thinking tools this belongs to, see our <a href=\"https:\/\/ceotudent.com\/en\/mental-models-that-matter-ai-era\/\">curated index of mental models that matter in the AI era<\/a>. The outside view pairs naturally with <a href=\"https:\/\/ceotudent.com\/en\/how-to-think-in-bets-probabilistic-decision-making\/\">thinking in bets<\/a>, which turns forecasts into probabilities, and with <a href=\"https:\/\/ceotudent.com\/en\/pre-mortem-protocol-kill-bad-plans-before-they-fail\/\">the pre-mortem<\/a>, which stress-tests a plan from the inside once the outside view has set realistic expectations. For a case study in how confident forecasts perform over time, see <a href=\"https:\/\/ceotudent.com\/en\/forecast-scorecard-decade-expert-ai-predictions-2016-2026\/\">our scorecard of a decade of expert AI predictions<\/a>. And if the base rate for your project looks grim, decide in advance what would make you stop: <a href=\"https:\/\/ceotudent.com\/en\/kill-criteria-decide-in-advance-when-to-quit-project-job-strategy\/\">kill criteria<\/a> are the outside view applied to quitting.<\/p>\n<h2 id=\"the-ceo-and-the-student-in-your-predictions\"><span class=\"ez-toc-section\" id=\"The-CEO-and-the-student-in-your-predictions\"><\/span>The CEO and the student in your predictions<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The CEO half of the CEOtudent lens is about capital allocation. A CEO who funded every project at its inside-view budget would run out of money; good capital allocators ask what similar investments returned before approving this one. Treat your own time, savings and career moves the same way. The student half is about calibration. The goal is not to distrust yourself forever but to learn how much your case knowledge is actually worth. Record your estimates, compare them with outcomes and let your personal predictability value rise only when your record earns it.<\/p>\n<h2 id=\"frequently-asked-questions\"><span class=\"ez-toc-section\" id=\"Frequently-asked-questions\"><\/span>Frequently asked questions<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>What is the outside view?<\/strong><br \/>\nA way of forecasting that starts from how similar cases have turned out, rather than from the details of your own case. The term comes from Kahneman and Lovallo (1993), who contrasted it with the inside view, which builds a scenario from the specifics of the plan.<\/p>\n<p><strong>What is a base rate?<\/strong><br \/>\nThe frequency of an outcome in a relevant group of past cases: for example, the share of new business establishments still operating after five years (51.4% for US establishments opened in the year to March 2020, according to the BLS). Base-rate neglect is the tendency to ignore this information in favor of case details.<\/p>\n<p><strong>What is reference class forecasting?<\/strong><br \/>\nBent Flyvbjerg&rsquo;s adaptation of Kahneman and Tversky&rsquo;s method for projects: identify a class of similar past projects, establish the distribution of their outcomes, and position your project within that distribution. The UK Department for Transport and HM Treasury adopted it in 2004 for large transport projects.<\/p>\n<p><strong>How do I combine a base rate with my own judgment?<\/strong><br \/>\nStart from the base rate and move toward your intuitive estimate in proportion to how predictive your judgment has proven to be: corrected estimate = base rate + predictability x (intuitive estimate &#8211; base rate). With a base rate of 4,000, an intuition of 12,000 and predictability of 0.6, the answer is 8,800.<\/p>\n<p><strong>Does training in base rates actually improve predictions?<\/strong><br \/>\nYes, in the best available evidence. In the Good Judgment Project&rsquo;s forecasting tournaments, a probabilistic reasoning module of less than an hour, which taught comparison classes and base rates, improved accuracy (Brier scores) by 6 to 12 percent depending on the year (the paper&rsquo;s abstract summarizes this as 6 to 11 percent), and the use of comparison classes and base rates was the principle most associated with gains.<\/p>\n<h2 id=\"sources\"><span class=\"ez-toc-section\" id=\"Sources\"><\/span>Sources<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ol>\n<li>Kahneman, D., and Tversky, A. (1977). Intuitive Prediction: Biases and Corrective Procedures. Technical Report PTR-1042-77-6, Decision Research. Published in TIMS Studies in Management Science, 12 (1979), 313-327.<\/li>\n<li>Kahneman, D., and Lovallo, D. (1993). Timid Choices and Bold Forecasts: A Cognitive Perspective on Risk Taking. Management Science, 39(1), 17-31.<\/li>\n<li>Buehler, R., Griffin, D., and Ross, M. (1994). Exploring the &ldquo;Planning Fallacy&rdquo;: Why People Underestimate Their Task Completion Times. Journal of Personality and Social Psychology, 67(3), 366-381.<\/li>\n<li>Bar-Hillel, M. (1980). The Base-Rate Fallacy in Probability Judgments. Acta Psychologica, 44, 211-233.<\/li>\n<li>Flyvbjerg, B. (2006). From Nobel Prize to Project Management: Getting Risks Right. Project Management Journal, 37(3), 5-15.<\/li>\n<li>Flyvbjerg, B. (2021). Top Ten Behavioral Biases in Project Management: An Overview. Project Management Journal, 52(6), 531-546.<\/li>\n<li>Chang, W., Chen, E., Mellers, B., and Tetlock, P. (2016). Developing Expert Political Judgment: The Impact of Training and Practice on Judgmental Accuracy in Geopolitical Forecasting Tournaments. Judgment and Decision Making, 11(5), 509-526.<\/li>\n<li>U.S. Bureau of Labor Statistics. Business Employment Dynamics: Survival of Private Sector Establishments by Opening Year (data through March 2025).<\/li>\n<li>Wong, C. H., Siah, K. W., and Lo, A. W. (2019). Estimation of Clinical Trial Success Rates and Related Parameters. Biostatistics, 20(2), 273-286; and corrigendum, Biostatistics, 20(2), 366.<\/li>\n<li>HM Treasury. Supplementary Green Book Guidance: Optimism Bias.<\/li>\n<\/ol>\n<p><em>Table 1 reports figures as published by the sources listed. Table 2 is the CEOtudent editorial framework: the base rates are published figures, while the intuitive estimates and predictability values are illustrative.<\/em><\/p>\n<hr>\n<p><em>This content was compiled with the support of AI following in-depth research, then written and prepared for publication by the CEOtudent editorial team.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Most bad forecasts are not failures of intelligence but of perspective: we reason from the details of our own case and ignore how similar cases usually turn out. This guide explains the outside view and base rates, collects verified base rates from project, business and drug-development data, and turns Kahneman and Tversky&#8217;s five-step correction into a worksheet with worked examples. In forecasting tournaments, less than an hour of training that included base rates improved accuracy by 6 to 12 percent.<\/p>\n","protected":false},"author":1,"featured_media":326182,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-326180","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/posts\/326180","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/comments?post=326180"}],"version-history":[{"count":0,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/posts\/326180\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/media\/326182"}],"wp:attachment":[{"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/media?parent=326180"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/categories?post=326180"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/tags?post=326180"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}