Calling BS in Startup Pitch Decks: Lessons from Jevin West

Numbers carry weight. They can persuade, intimidate, and even mislead. At a recent Alliance of Angels event, Jevin West, Associate Dean for Research at the University of Washington and co-founder of the Center for an Informed Public, shared insights on spotting misleading statistics and data pitfalls in startup pitch decks.

Drawing from both academia and startup experience, West highlighted how poor data use can undermine credibility and distract from otherwise strong ideas. For both investors and entrepreneurs, his message was clear: clarity and rigor beat hype every time.

The Problem with Data in Pitch Decks

Founders often lean on statistics to prove traction, market opportunity, or product effectiveness. However, the sloppy or misleading use of data through truncated axes, cumulative plots, inconsistent scales, or recycled “zombie statistics” can be the difference between receiving funding or not. West is on a mission for the truth through all the BS in the world. As he defines it,“Bullsh*t involves language, statistical figures, data graphics, and other forms of presentation intended to impress, overwhelm, or persuade, often with disregard for logic or truth.” Sometimes these issues are intentional, but more often they’re the result of oversight or misplaced enthusiasm. Either way, the outcome is the same: the founder's credibility takes a hit. Jevin West, however, made sure to hammer home the point of Hanlon’s Razor, to “never attribute to malice that which is adequately explained by stupidity.”

Investors and founders, read this article carefully, as beyond just pitch decks, data manipulation occurs at companies and governments alike. The truth may not always be what we want, but it is always better to know it.

Common Pitfalls To Identify

Hype & Hyperbole

“Extraordinary claims require extraordinary evidence.” - Jevin West

Hype and hyperbole can lead to bad investment decisions. Investor FOMO is real and AI is a great example. In the scientific space, claims such as AI can completely automate the scientific method and peer review attract attention and even funding. These claims have yet to be validated as we see scientific papers often coming under fire for their use of AI. This ends up overshadowing the real, incremental advances happening under the hood. Hype might bring in the money, but without substance, it wastes time and capital in the long run.

Logical & Statistical Fallacies

Non sequiturs: Citing $2.7T in U.S. transportation costs without showing how your product addresses them.

Zombie statistics: Debunked myths such as that “we only use 10% of our brains” which survive because they sound persuasive.

Authority through numbers: Bold fonts and large round figures (25%, 50%, 75%) look impressive, but they often lack substance.

“Mathiness” & Algebraic Shock

Complex formulas or models sometimes show up in decks to intimidate. If the math doesn’t connect to the business case, it’s noise.

Bad Visualizations

From misused Venn diagrams to manipulated axes, poor graphics often confuse rather than clarify. A favorite trick of corporations and our government? Cumulative graphs always go up, allow them to mask quarter-to-quarter declines.

Why It Matters

Statistics carry an authority that words alone do not. Misused data can be more persuasive, and more damaging, than verbal exaggeration.

For founders: transparency and intellectual honesty are non-negotiable.

For investors: vigilance is critical. A slick deck can disguise a weak case.

Key Questions Investors Should Ask

Simply asking a few clear, skeptical questions exposes 80–90% of weak claims according to Jevin West:

• What exactly is being measured?

• Who collected the data, and how?

• Are the numbers connected to the actual business case, or are they window dressing?

• Are references legitimate, or are they AI-generated hallucinations?

• Does the claim actually matter in a real, measurable way?

  
  

West quipped back to the audience saying, “I want to invest in you guys, you’re asking all the right questions” after driving investors and founders alike to break down why certain example statistics could be misleading.

Deeper Pitfalls

West then illustrated how subtle data issues can distort conclusions:

• Selection bias.

  • Insurance ads often promise “average $500 savings,” but only because they measure customers willing to switch; wellness programs look effective only because healthier people sign up.

• Simpson’s paradox

  •  Aggregate data shows one trend, but subgroup analysis flips the conclusion, often yielding more compelling correlation analysis.

• Outcome switching

• Startups sometimes pivot mid-study, claiming success in a subgroup after failing in the overall population.

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The lesson: most problems don’t lie in the algorithms or visuals, they lie in how the data is chosen and interpreted. People can choose any story they want to tell and back it up statistically. As investors, conscious detection and identification of these deep rooted pitfalls may be difficult to do, but it is still necessary to ask yourself if the data you see has been manipulated.

Everyone thinks they know the difference between correlation and causation, but the hard truth is that even scientists struggle with causality.

• A viral COVID study claimed men were at higher risk of severe outcomes due to hormones.

• Reality: age was the confounding variable.

• Media, companies, and even startups built on this shaky claim before it collapsed.

Causal traps to watch for:

• Reverse causality.

• Hidden confounders.

• Correlation conflation.

• Subgroup vs. population-level effects.

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The Double-Edged Sword of AI

AI has the power to transform industries, but it also poses risks by sometimes generating convincing yet misleading information.

• For example, academic journals have published articles containing the phrase “As an AI language model…”, revealing inappropriate AI content in formal settings.

• Fake citations have slipped into legal testimony.

• Hallucinated numbers, like a 75th percentile lower than the mean, appear in reports.

  
  

AI is fluent and confident, qualities that seduce investors and founders alike into believing falsehoods. As West warned: “As in any fairy tale, accepting magical assistance comes with risks.”

Final Thought

West left with a call to action, we, the Alliance of Angels, couldn’t agree with more: aim to build a culture of critical thinking. Critical thinking isn’t just an individual act; it’s a collective practice. Like aviation safety protocols or surgical checklists, startups and investors should foster a culture where questioning, skepticism, and “calling BS” are welcomed. Every founder, investor, and researcher has unconscious reasons to BS, and in an era where AI amplifies both knowledge and misinformation, clarity and transparency are more valuable than ever.

For founders: don’t fool yourself. Build peer networks that poke holes in your claims before pitching.

For investors: remember Hanlon’s Razor, most mistakes come from enthusiasm or incompetence, not malice. But don’t forget: hype without rigor costs real money.

“Calling BS isn’t about making you look smarter, it’s about making everyone smarter.” - Jevin West