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  <description>Big ideas from big samples.</description>
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    <title>Born in a Press Release, Raised by Aggregators: The Strange Life Cycle of a Viral Study</title>
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    <description>One small survey goes in, forty identical headlines come out. We follow a single study&#039;s journey through the American news machine and map exactly where the skepticism should have kicked in — and didn&#039;t.</description>
    <author>N Equals Many</author>
    <category>Statistics Myths</category>
    <pubDate>Mon, 02 Nov 2026 08:07:16 GMT</pubDate>
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    <title>The Medal Goes to Whoever Showed Up: How Beer Competitions Confuse a Crowd with a Census</title>
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    <description>Every year, some brewery walks away with a medal and a marketing budget built on the phrase &#039;Best Beer in America.&#039; Here&#039;s what those competitions actually measure — and why the honest answer is &#039;a handful of thirsty judges on one afternoon.&#039;</description>
    <author>N Equals Many</author>
    <category>Statistics Myths</category>
    <pubDate>Mon, 02 Nov 2026 00:07:41 GMT</pubDate>
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    <title>The Control Group Knew. It Worked Anyway. Now What?</title>
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    <description>Open-label placebo trials keep producing a result that shouldn&#039;t exist: patients get better from pills they know are empty. Here&#039;s why that finding is messier than the headlines suggest — and what it exposes about the sacred architecture of the randomized controlled trial.</description>
    <author>N Equals Many</author>
    <category>Statistics Myths</category>
    <pubDate>Sun, 01 Nov 2026 12:10:55 GMT</pubDate>
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    <title>The Toughest Opponent in Medicine Is a Pill Made of Nothing</title>
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    <description>Sugar pills keep winning, and it&#039;s not because they&#039;re magical. It&#039;s because the bar they&#039;re measured against — the humble placebo response — has been quietly climbing for decades. Here&#039;s what that means for every &#039;breakthrough drug&#039; headline you&#039;ll read this year.</description>
    <author>N Equals Many</author>
    <category>Statistics Myths</category>
    <pubDate>Sun, 01 Nov 2026 08:11:12 GMT</pubDate>
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    <title>Twelve Reviews Can&#039;t Hurt You: The Quiet Math Behind Every Star Rating You&#039;ve Ever Trusted</title>
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    <description>That 4.9-star taco truck with a dozen reviews and the 3.8-star diner with three thousand are telling you less than you think. Here&#039;s how sample size, Bayesian smoothing, and a little review gaming decide where you eat lunch.</description>
    <author>N Equals Many</author>
    <category>Statistics Myths</category>
    <pubDate>Sun, 01 Nov 2026 00:13:18 GMT</pubDate>
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    <title>Your Heatwave Is Not a Trend: The Enormous Math Behind One Global Thermometer</title>
    <link>https://nequalsmany.com/heatwave-is-not-a-trend-global-thermometer-math/</link>
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    <description>Every summer, a record-breaking heatwave or a freak blizzard gets trotted out as proof of whatever someone already believed about climate. Here&#039;s why one storm settles nothing — and how scientists stack thousands of weather stations and a century of data to find a signal that refuses to hide.</description>
    <author>N Equals Many</author>
    <category>Statistics Myths</category>
    <pubDate>Sat, 31 Oct 2026 16:10:54 GMT</pubDate>
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    <title>The Universe Loves Averages: How Randomness Writes Your Supplement&#039;s Five-Star Reviews</title>
    <link>https://nequalsmany.com/universe-loves-averages-randomness-supplement-reviews/</link>
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    <description>Your worst acne flare-up was always going to get better on its own — but the supplement you started that week gets the credit. Welcome to regression to the mean, the invisible force behind millions of glowing testimonials.</description>
    <author>N Equals Many</author>
    <category>Statistics Myths</category>
    <pubDate>Sat, 31 Oct 2026 12:16:23 GMT</pubDate>
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    <title>Garbage In, Billions Out: How Badly Worded Questions Spent Your Tax Dollars</title>
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    <description>The math was fine. The arithmetic checked out. What failed was the question itself — and when America asks the wrong question, the answer comes back with a ten-figure price tag. Five cases where measurement design, not calculation, steered fortunes.</description>
    <author>N Equals Many</author>
    <category>Data in History</category>
    <pubDate>Sat, 31 Oct 2026 12:16:23 GMT</pubDate>
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    <title>Two Million Votes, Zero Clue: The Polling Disasters That Changed America</title>
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    <description>The 1936 Literary Digest poll gathered 2.4 million responses and still called the election wrong. The problem was never the size of the sample — it was asking the wrong Americans. Five polling disasters that prove a big sample of the wrong people is just a confident mistake.</description>
    <author>N Equals Many</author>
    <category>Data in History</category>
    <pubDate>Sat, 31 Oct 2026 08:10:03 GMT</pubDate>
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    <title>You Are a Sample Size of One (and Your Fitness Tracker Knows It)</title>
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    <description>Your sleep app says coffee is wrecking your nights, and honestly, the chart looks convincing. But there&#039;s a difference between the data of you and the science of many — and knowing which one you&#039;re holding changes everything.</description>
    <author>N Equals Many</author>
    <category>Quantified Self</category>
    <pubDate>Sat, 31 Oct 2026 08:10:03 GMT</pubDate>
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  <item>
    <title>Congratulations, You&#039;re in the Top 10% (of a Group Someone Sliced Until You Were)</title>
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    <description>&#039;Top 10% hospital.&#039; &#039;Top-rated employer.&#039; &#039;Above-average school district.&#039; America runs on ranking math, and most of it is a magic trick performed with small groups and selective slicing. A field guide to spotting the racket.</description>
    <author>N Equals Many</author>
    <category>Data in History</category>
    <pubDate>Sat, 31 Oct 2026 04:10:34 GMT</pubDate>
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  <item>
    <title>82% Irish, Give or Take a Continent: The Shaky Math Inside Your Spit Tube</title>
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    <description>Your DNA test says you&#039;re mostly Irish. The confidence interval says it could say almost anything. Here&#039;s why ethnicity estimates are the flashiest, least reliable numbers in consumer science.</description>
    <author>N Equals Many</author>
    <category>Statistics Myths</category>
    <pubDate>Sat, 31 Oct 2026 04:10:34 GMT</pubDate>
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    <title>When the Map Lies: Five American Headcounts That Went Sideways</title>
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    <description>Every ten years, America counts itself — and every ten years, the count comes up short somewhere. From the 1990 undercount to the 2020 fiasco, here&#039;s what happens when millions of people vanish from the ledger.</description>
    <author>N Equals Many</author>
    <category>Data in History</category>
    <pubDate>Sat, 31 Oct 2026 00:10:49 GMT</pubDate>
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    <title>The Trial Was Doomed Before It Started: How Big Pharma Designs Studies to Miss the Point</title>
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    <description>&#039;No significant difference found&#039; sounds like a verdict. Often, it&#039;s just an alibi. Here&#039;s why an underpowered clinical trial can burn through billions and still tell us absolutely nothing.</description>
    <author>N Equals Many</author>
    <category>Statistics Myths</category>
    <pubDate>Sat, 31 Oct 2026 00:10:49 GMT</pubDate>
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    <title>The Ghost Experiment: One Ordinary Tuesday, 47 Tests Deep</title>
    <link>https://nequalsmany.com/ghost-experiment-ab-testing-ordinary-tuesday/</link>
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    <description>Follow a hypothetical American through a single day — waking, scrolling, streaming, shopping — and count the experiments running on her without her knowledge. The A/B testing industry is the largest behavioral science program in human history. Nobody signed a consent form.</description>
    <author>N Equals Many</author>
    <category>Data in History</category>
    <pubDate>Fri, 30 Oct 2026 20:11:12 GMT</pubDate>
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    <title>Statistical Tie or Sure Thing? The Two Words That Ruin Every Election Night</title>
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    <description>Every November, millions of Americans watch a candidate&#039;s &#039;lead&#039; evaporate and conclude the polls were broken. They weren&#039;t. Here&#039;s what margin of error actually means, why &#039;up by two&#039; can mean nothing at all, and how to read a poll like a data scientist instead of a doomscroller.</description>
    <author>N Equals Many</author>
    <category>Statistics Myths</category>
    <pubDate>Fri, 30 Oct 2026 20:11:12 GMT</pubDate>
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    <title>Millions of Lives, a Handful of Data Points: Five Small Samples That Shook America</title>
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    <description>The fate of a drug, an election, and even a war has sometimes hinged on a sample so small it would fail an intro stats class. Five American stories where the n was tiny — and the stakes were anything but.</description>
    <author>N Equals Many</author>
    <category>Data in History</category>
    <pubDate>Fri, 30 Oct 2026 16:10:31 GMT</pubDate>
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    <title>Where Did the &#039;Sample Size of 30&#039; Rule Come From? (And Why It Needs to Retire)</title>
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    <description>Ask a college grad how many people you need for a &#039;real&#039; study and you&#039;ll hear the same number: 30. Here&#039;s how a piece of 20th-century textbook shorthand became one of the most stubborn misconceptions in American classrooms — and what actually determines whether a sample is big enough.</description>
    <author>N Equals Many</author>
    <category>Statistics Myths</category>
    <pubDate>Fri, 30 Oct 2026 16:10:31 GMT</pubDate>
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