{"id":7059825,"date":"2026-09-17T08:35:12","date_gmt":"2026-09-17T08:35:12","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/googles-statistics-heads-they-win-tails-you-lose\/"},"modified":"2026-09-17T08:35:12","modified_gmt":"2026-09-17T08:35:12","slug":"googles-statistics-heads-they-win-tails-you-lose","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=7059825","title":{"rendered":"Google’s Statistics: Heads They Win, Tails You Lose."},"content":{"rendered":"


\n<\/p>\n

\n

When Google reports lift results\u2026 Who is it solving for?<\/p>\n

Let\u2019s start with a phrase that causes me to cringe every time I hear it: \u201cdirectional results.\u201d<\/p>\n

It sounds sophisticated. Nuanced. Even reasonable. But what does it actually mean?<\/p>\n

Answer: Just about anything you want it to mean!<\/p>\n

I was reminded of this while reading Google\u2019s official guidance<\/a> on how to interpret Google\/YouTube\u2019s Brand Lift, Search Lift, and Conversion Lift studies.<\/p>\n

\"Google's<\/center><\/p>\n

It made me pause. Then sad. Read the last one carefully.<\/p>\n

Google says: \u226590% is \u201cvery good chance.\u201d 70%-90% is \u201cgood chance.\u201d 50%-70% is \u201cmoderate chance.\u201d <50% is “no lift.”Across three layers the sentiment emphasized is, paraphrasing: Your ads probably <\/strong>did something good.<\/em><\/p>\n

The last layer is, paraphrasing: Hey, the ads might not have sucked, spend some more and let\u2019s see what happens.<\/em><\/p>\n

Google also says in the article: Studies at 50%+ can provide valuable, directional insights, and explicitly tells advertisers in the 50%-70% band to use the results directionally.<\/p>\n

If you pause and reflect on this carefully caveated article, you\u2019ll see this is a case of: Heads Google wins, tails you lose.<\/strong><\/em><\/p>\n

This post was first published as TMAI Premium #523<\/strong>. My weekly newsletter cuts through the noise: Strategic frameworks, actionable advice, zero fluff \u2013 built on decades of doing, not just advising. For serious Marketing and Analytics professionals. Subscribe today.<\/a><\/div>\n

\u00a0
Whose Success Matters More?<\/strong><\/span><\/p>\n

To bring this lesson home, let\u2019s assume I ran a campaign on YouTube.<\/p>\n

  • Investment: $5 million<\/strong><\/li>\n
  • Brand KPI: Consideration<\/strong><\/li>\n
  • YouTube-reported BLS result: +3-point lift<\/strong><\/li>\n
  • YouTube-reported Certainty: 70%<\/strong><\/li>\n

    \u00a0
    Google calls 70%-90% Certainty a \u201cgood chance.<\/em>\u201d It also says studies at 50%+ can provide \u201cvaluable, directional insights.<\/em>\u201d<\/p>\n

    What. The. Heck. Does. Directionally<\/em> mean?<\/p>\n

  • Do I spend another $5 million on another YouTube campaign?<\/li>\n
  • Do I spend less and do something different, in a different place?<\/li>\n
  • Do I change the creative or media tactics?<\/li>\n
  • Do I shift the money to Meta that presented a new exciting idea yesterday?<\/li>\n
  • Do I run another \u201ctest\u201d (and to test what)?<\/li>\n

    \u00a0
    From Google\u2019s view, the interpretation is that there is a \u201cgood chance the results were caused by your ads.<\/em>\u201d But as a Senior Director of Brand Media, is a \u201cgood chance\u201d good enough for me to report to my CFO and ask for $5 mil more?<\/p>\n

    Two Parties. Two VERY Different Consequences.<\/strong><\/span><\/p>\n

    Party One: Google:<\/strong> It sells advertising. If a study contains weak evidence that advertising could possibly have worked, Google has every incentive not to throw it away. Perhaps, and that\u2019s the operative word, perhaps there is something <\/em>useful to learn. Fair enough.<\/p>\n

    Party Two:<\/strong> You: You own the Marketing budget. Your job is not to find something interesting in the data.<\/em> Your job is to answer: IS the evidence in YouTube\u2019s BLS strong enough that I should risk another $5 million of my company\u2019s money on the conclusion<\/strong> that the first $5 million worked?<\/p>\n

    Here, I made a little table to outline incentives and implications:<\/p>\n

    \"Google<\/center><\/p>\n

    It is impossible to know if Google\u2019s wording on its sites, in its client decks, is intentionally self-serving. It does not matter. The outcome is self-serving.<\/strong><\/p>\n

    Google does not<\/strong> bear the consequences of an optimistic interpretation. Your company does.<\/strong> Google also, to their credit, has a drive-by line that says: Guys, interpret results based on your business needs and risk tolerance.<\/em><\/p>\n

    Since I am bearing the risk, for consequential business decisions I use 90% Certainty as the floor<\/u>. It isn\u2019t magic; it is simply the minimum level of evidence I\u2019m willing to accept before telling a CMO or CFO: \u201cThis worked. Spend again.<\/em>\u201d<\/p>\n

    [<\/span><\/strong>Premium Subscribers: Refer to TMAI #298: Smarter Statistical Significance Reporting.<\/em> It includes a one-pager to standardize stat sig reporting across your team, agency, vendors. If you can\u2019t find it, please email me.]<\/span><\/strong><\/p>\n

    Google\u2019s emphasis is on \u201cmoderate chance,\u201d \u201cgood chance,\u201d \u201cdirectionally.\u201d These are comforting, decision-sounding words that obfuscate evidence more than illuminate it. The language is for sure carefully caveated, but it ignores the immense risk and cost it intentionally or unintentionally shifts onto Google\u2019s clients.<\/p>\n

    Let me prove it to you.<\/p>\n

    CMO, Meet Replication Risk.<\/strong><\/span><\/p>\n

    Rather than discuss p-values associated with \u201cgood chance,\u201d \u201cmoderate chance,\u201d \u201cuse these results directionally,\u201d let\u2019s focus on the question your CMO and CFO care about:<\/p>\n

    If we ran this campaign again with another $5m budget, how likely is it that we will get a result strong enough to confidently say: \u201cRound two worked. Spend again.\u201d<\/strong><\/p>\n

    You\u2019ll recall YouTube reported a +3-point lift in Consideration at 70% Certainty.<\/p>\n

    While they might not use the technical phrase, your CMO and CFO are essentially asking: What\u2019s the Replication Risk?<\/em><\/em><\/p>\n

    [<\/strong>For this thought experiment, we\u2019ll assume, the second $5m will face the same underlying conditions as the first $5m. Same platform (doh!), audience, seasonal factors, promotions, creative, sample size, etc.]<\/strong><\/p>\n

    To compute Replication Risk, I\u2019ll use a simple Bayesian replication model inspired by biostatistician Steven Goodman<\/a>. It helps translate Google\u2019s abstract \u201cCertainty\u201d into a much more practical question: What might happen if we measure the same thing again?<\/em><\/p>\n

    YouTube\u2019s BLS estimated a +3-point lift in Consideration. But +3 is an estimate; rather than pretending it is precise, the Bayesian approach carries that uncertainty forward into the next measurement.<\/p>\n

    As business owners, it is our money on the line. We are applying this simple logic:<\/p>\n

    (Uncertainty from what the true effect might be)
    combined with
    (Uncertainty from measuring it again)
    = How confident should we be that we will see similar results?<\/p>\n

    Let\u2019s do some math!<\/p>\n

    Three computations for the Business Owner lens:<\/p>\n

    \n

    1.<\/strong> Repeat Shows Positive Lift: The probability thta an equivalent repeat produces any positive lift, even a tiny, tiny, tiny +0.01 points \u2013 an extremely low bar.<\/p>\n

    2.<\/strong> Repeat Reaches \u226590% Certainty: The probability that an equivalent repeat produces \u226590% Certainty \u2013 my minimum standard for evidence strong enough for us to spend again.<\/p>\n

    3.<\/strong> Replication Risk: Simply the opposite of #1. The probability that the repeat falls short of the \u226590% standard.<\/p>\n

    Human language: What are the chances I spend another $5m, and still don\u2019t get a result with certainty for me tell my CMO or CFO, \u201cThis worked. Spend again.\u201d<\/p>\n

    Punch to the gut: If your Replication Risk is 70%\u2026 There is only a 30% chance that spending another $5m will result in you telling your CMO \u201cThis worked.\u201d #killmefirst<\/p>\n

    Google vs. You: Google calls 70% Certainty a \u201cgood chance.\u201d The replication math shows the \u201cmuch, much less than good chance\u201d you\u2019ll take in round two.<\/p>\n<\/div>\n

    Here\u2019s the model using a simple Bayesian replication model inspired by Mr. Goodman:<\/p>\n

    \"Results<\/center><\/p>\n

    [<\/strong>Pedantic Note: Google says displayed Certainty is rounded down in 5-point increments. So a displayed 70% can represent 70% to just under 75% and hence the exact replication % can move a few points. For simplicity, I treat Google\u2019s displayed Certainty as exact.]<\/strong><\/p>\n

    YouTube had reported 70% \u201cCertainty,\u201d follow the row above\u2026 That \u201cgood chance\u201d Google label translates into:<\/p>\n

    \n

    1. Just a 65% (!) chance of any Positive Lift, with a<\/p>\n

    2. A puny 30% chance of hitting high certainty,<\/p>\n

    3. when repeating the same spend ($5m) under the same conditions.<\/strong><\/p>\n<\/div>\n

    As a business owner, would you feel confident saying yes, let\u2019s spend on YT again?<\/em><\/p>\n

    Forget Google for a second, take me: I recommend a minimum of 90% Certainty<\/strong> for a business decision. That comes with a Replication Risk of 50% (!!) and Repeat Shows Positive Lift of just 82%. Too uncomfortable in many cases (what if it was a $10m question, would you take 50% and 82%?)<\/p>\n

    Now Alex and Juliette understand why I so often push for 95% Certainty<\/strong>. When you spend as much money as we do on YouTube\u2026 At least get the Replication Risk down to 40%, increase the Repeat Shows Positive Lift to 88% and increase the odds more in the company\u2019s favor.<\/p>\n

    So\u2026 Are results below 90% Certainty useless?<\/p>\n

    If you have real money riding on it, look at column 3, look at column 5 in the table, see if you want those odds. If you have little to no money riding on it, sure \u2013 go try a new hypothesis, design a different\/better test, give additional money to Google\/YT\/Meta\/TT, remeasure.<\/p>\n

    A bonus relevant lesson from \u201cBeyond Power Calculations: Assessing Type S (sign) and Type M (magnitude) Errors\u201d: Weak evidence can hurt twice: The evidence may not survive (70% in our case), and the apparent impact might be overstated (+3 in our case).<\/p>\n

    You\u2019ve been warned.<\/p>\n

    Premium Subscriber Bonus: A Working Model.<\/strong><\/span><\/p>\n

    I want you to be able to see all the formulas and math I\u2019ve used in my computation of Replication Risk.<\/p>\n

    I want you to be able to test lift\u2019s Certainty AND Magnitude (Repeat Shows Positive).<\/p>\n

    I want you to be able to type in the results of your Brand Lift, Search Lift, and Conversion Lift studies and calculate your own truth (before you put your career on the line).<\/p>\n

    To enable all this, I\u2019ve built a working model with Read Me + Methodology, Calculator, Risk Table, and Calculate Your Truth tabs. It is a bonus for TMAI Premium subscribers<\/a>; please just email me for lift_replication_risk_model.xlsx.<\/p>\n

    Bottom line.<\/strong><\/span><\/p>\n

    Google and you are sitting on opposite sides of a consequential decision.<\/p>\n

    Google sells advertising. You are spending money.<\/p>\n

    Google might <\/strong>reasonably find value in evidence that is merely suggestive. See the first table. You have to decide whether that evidence is sufficient and measured credibly enough to risk the next tranche of your budget.<\/p>\n

    Don\u2019t let an advertising platform\u2019s definition of useful information<\/em> become your company\u2019s definition of sufficient evidence<\/em>.<\/p>\n

    Stop outsourcing your tolerance for risk to the company selling you ads or the Agency whose fees are tied to the size of your spend.<\/p>\n

    Carpe diem.<\/p>\n

    PS:<\/strong> We have used Google\u2019s specific table and specific guidance today. Replication risk applies just as much to Meta, TikTok, and confident calcs your Agency is sending you. Please check just as carefully if they are solving for themselves or you.<\/p>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

    When Google reports lift results\u2026 Who is it solving for? Let\u2019s start with a phrase that causes me to cringe every time I hear it: \u201cdirectional results.\u201d It sounds sophisticated. Nuanced. Even reasonable. But what does it actually mean? Answer: Just about anything you want it to mean! I was reminded of this while reading […]<\/p>\n","protected":false},"author":1,"featured_media":7059826,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[182810,13754,2559,177031,15985,11602,22259,4666],"dealstore":[],"offerexpiration":[],"class_list":["post-7059825","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-actionable-analytics","tag-googles","tag-heads","tag-key-performance-indicators","tag-lose","tag-statistics","tag-tails","tag-win"],"yoast_head":"\nGoogle's Statistics: Heads They Win, Tails You Lose. - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=7059825\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Google's Statistics: Heads They Win, Tails You Lose. - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"When Google reports lift results\u2026 Who is it solving for? 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