Imperfect Stat Stack
A run of several uneven, oddly exact figures reported together, with the weak ones left in, so the set reads as counted rather than chosen.
Definition
What it does
Instead of leading with one flattering number, the copy reports a whole set of results at once, each with its own irregular figure, and keeps the low ones in the list. A 17 sits next to a 93. A modest result follows a big one. The reader stops reading a claim and starts reading a record. The spread across the numbers, not the size of any single one, is what carries the proof.
Why it works
Round, evenly spaced numbers read as authored. Uneven ones read as measured. A single figure invites the question of what got left out, and a stack answers that question before it is asked by showing the range, including the results nobody would pick for a headline. Leaving a poor number in costs the writer something, and people treat a costly admission as more honest than a clean one. The set also gives the eye something to inspect, which feels like checking evidence rather than being handed a conclusion.
Where it appears
* Most frequent first, based on materials selected for the Persuasion Taxonomy corpus.
Examples of Imperfect Stat Stack
“you could have gained 62.3%, 31.6%, 93.6%, 28.7%, 54.1%, 17.5%, and 52.4%”
A penny stock advisory reports what its rating system averaged per trade and then lists the individual results behind that average.
Why it’s this techniqueSeven returns arrive at one decimal place apiece, '62.3%, 31.6%, 93.6%, 28.7%, 54.1%, 17.5%, and 52.4%', with the average stated as '33.8%' and the compounded figure carried to the cent at '$3,677.83'. The uneven precision does the work: numbers this ragged read as transcribed from a record instead of composed for a headline. The tell is the mix. A deliberately small '17.5%' sits beside the '93.6%' in unsorted order, so the run declines the flattery a single round number would carry. The claim rests on the texture of the digits, not on the named method behind them.
Classification
- Primary technique
- PT-PRV-9507
- Classification confidence
- 0.90
- Source
- CXS Penny Stock Fortunes, 2000s direct mail
“✅85% of participants felt more energy† ✅70% noticed less gas and bloating† ✅84% felt less tired in the afternoon†”
A greens powder ad reports self-reported outcomes from its own three month study and spells out the study design in a footnote.
Why it’s this techniqueThree benefit claims arrive as a run of odd figures: '85% of participants felt more energy', '70% noticed less gas and bloating', and '84% felt less tired in the afternoon'. None rounds to a clean number and none reaches 100, so the ragged spread reads as measurement rather than marketing. The structural tell is that the weakest figure stays in the stack instead of being cut, and the order refuses to climb, which is what separates this from a straight proof list. The study footnote supplies backing, but the belief is carried by the shape of the numbers.
Classification
- Primary technique
- PT-PRV-9507
- Classification confidence
- 0.85
- Source
- AG1, 2020s ad
“generated an accuracy rate of 81% and total gains of 1,164% on its closed picks! Folks who followed the 'WaveStrength 2004 All Stars' picks realized gains of 155%... 159%... 71%...28%...27%...and more!”
A stock alert email reviews the prior year's published picks and the gains each of them closed at.
Why it’s this techniqueThe numbers arrive rough and out of order. An '81%' hit rate sits beside total gains of '1,164%', then individual winners drop in unsorted: '155%... 159%... 71%...28%...27%...and more!' The descent is uneven, 159% lands after 155%, and small results stay in the run rather than being pruned to the highlights. That raggedness is the tell. A polished claim would round, sort, and stop at the biggest figure; here the ugly precision and the modest tail do the persuading, because a list nobody bothered to clean up reads as a ledger rather than a pitch. The date stamp only certifies the ledger.
Classification
- Primary technique
- PT-PRV-9507
- Classification confidence
- 0.88
- Source
- WaveStrength Trader, 2000s email
“79% Don't understand Cyber Security 80% Don't use data protection 91% Don't use endpoint mobile security 83% Don't have a plan for cyber security 70% Don't have an adequate backup system”
A cyber security pitch opens with a block of survey findings about how unprepared small businesses are.
Why it’s this techniqueFive figures land in a column and none of them rounds off: '79%', '91%', '83%', values ragged enough to read as counted rather than asserted. Each attaches to a separate exposure, 'Don't use data protection', 'Don't have a plan for cyber security', so the reader cannot wave off one number without facing four more. The tell is repetition of an identical frame across every line with no source, no explanation, and no single figure asked to carry the argument. Accumulated bulk persuades. Agitation of risk rides along, but the unrounded values and the stacking are what the copy is built on.
Classification
- Primary technique
- PT-PRV-9507
- Classification confidence
- 0.82
- Source
- Quentin, 2010s web page
“Several studies show that it can: Cut TOTAL cholesterol as much as 17%... Slash LDL cholesterol by 25%... Raise HDL ('good') cholesterol up to 29%... Drop dangerous triglycerides as much as 18%...”
A health newsletter promotion lists what published trials report a plant extract does to four separate blood markers.
Why it’s this techniqueFour claims arrive as a run of odd, unrounded figures, '17%', '25%', '29%', '18%', each capped by a ceiling phrase, 'as much as' and 'up to', so the numbers read as measured results rather than sales rounding. The tell is the spread: the values refuse a pattern, they are not multiples of five, and they do not climb in order, which is what a real study table looks like and what invented numbers do not. A cited frame, 'Several studies show', hands the arithmetic to outside record keepers, so the reader audits the digits instead of the promise.
Classification
- Primary technique
- PT-PRV-9507
- Classification confidence
- 0.80
- Source
- Agora, 2000s direct mail
See whether your own copy uses Imperfect Stat Stack, and what else it is doing: analyze your copy.
Boundary Conditions
When it lands
- The figures come from one real measured set, a portfolio, a trial, a customer survey, so the range between them is genuine
- At least one number in the run is ordinary or disappointing and is left exactly where it fell
- Each figure carries its own unit or window, a percent, a count, a number of days, so a reader could go check it
- The claim the numbers support is modest enough that the spread backs it up instead of undercutting it
When it dilutes
- Every figure in the run is a winner, which turns the stack back into a highlight reel
- The numbers are rounded or evenly spaced, such as 20, 40 and 60 percent, which reads as written rather than counted
- The denominator is missing, so a reader cannot tell how many results were left out of the list
- The run keeps going well past six or seven items and the eye stops reading figures and starts skipping them
Taxonomic Relationships
- PT-PRV-9374Aggregate-Cohort Dollar Result
- PT-PRV-9590Case Study Metrics
- PT-PRV-9576Clinical Proof
- PT-PRV-9497Comparison Statistics
- PT-PRV-9131Count-Plus-Habit Proof
- PT-PRV-9407Dual Metric Juxtaposition
- PT-PRV-9073Exact-Dollar Proof
- PT-PRV-9342Ingredient-Count Maximalism
- PT-PRV-9100Inventory Depth Authority
- PT-PRV-9738Only-One-With-a-Trial Proof
- PT-PRV-9486Peak Load Reliability Proof
- PT-PRV-0111Precise Statistics
- PT-PRV-9187Proprietary Research
- PT-PRV-9925Research Citations
- PT-PRV-9812The One Thing Nobody Else Does
Provenance
- Claude Hopkins, Scientific Advertising (1923), on specific figures outpulling general claims
- John Caples, Tested Advertising Methods, on tested specificity and the pull of exact numbers over broad assertions
- Elliot Aronson, Ben Willerman and Joanne Floyd, 'The Effect of a Pratfall on Increasing Interpersonal Attractiveness' (1966), on a visible flaw raising credibility