Post Hoc Ergo Propter Hoc | Logical Fallacies | Sales Repository

Establish cause-and-effect relationships to influence buyer decisions and reinforce product value

Introduction

Post Hoc Ergo Propter Hoc—Latin for “after this, therefore because of this”—is a classic logical fallacy that mistakes sequence for cause. It assumes that if event B follows event A, then A must have caused B. While often intuitive, this reasoning misleads by ignoring alternative explanations, confounding variables, and coincidence.

This article explains how Post Hoc Ergo Propter Hoc operates, why it persuades even skilled communicators, and how to recognize and counter it across business, media, and sales contexts.

Sales connection: In sales conversations, this fallacy often surfaces in ROI claims (“clients who used us doubled revenue”), customer churn explanations (“they left after the new pricing model”), or competitor framing (“they switched to us and grew fast”). When causal shortcuts slip in, trust erodes, close rates dip, and long-term retention suffers.

Formal Definition & Taxonomy

Definition:

Post Hoc Ergo Propter Hoc is the fallacy of assuming a causal relationship solely because one event follows another in time. The reasoning form is:

1.A occurred before B.

2.Therefore, A caused B.

Formally, it’s a causal fallacy and a subset of non-sequitur arguments (the conclusion does not logically follow from the premises). It sits within the fallacies of presumption, where an unwarranted assumption underlies the argument.

Taxonomy:

•Category: Informal fallacy

•Type: Fallacy of causal inference

•Family: Relevance → Causal → False cause

Commonly confused fallacies:

•Cum Hoc Ergo Propter Hoc: Assumes causation from correlation (“X and Y happen together, so one causes the other”).

•Hasty Generalization: Draws a broad conclusion from limited cases. Post Hoc focuses specifically on temporal order rather than sample size.

Sales lens:

This fallacy shows up during:

•Inbound qualification: Assuming lead quality improved because a new script launched.

•Discovery: Inferring that pain points disappeared due to one intervention.

•Demo: Claiming a feature directly caused prior client success.

•Proposal: Suggesting adoption will guarantee ROI seen elsewhere.

•Negotiation/Renewal: Linking timing of results or churn to one isolated event.

Mechanism: Why It Persuades Despite Being Invalid

The Reasoning Error

The structure feels plausible because humans are pattern-seeking: we connect events chronologically and infer causality to make sense of complexity. The brain prefers coherent stories to uncertain randomness, even when the data don’t justify the link.

Cognitive Principles Behind the Fallacy

Cognitive Principle	Description	Sales Example
Availability bias	We recall vivid, recent examples and treat them as proof of causation.	A rep cites one success story to “prove” feature X drives ROI.
Illusory causation	We over-attribute cause when two events co-occur or follow sequentially.	“After switching CRMs, churn dropped—so the CRM fixed retention.”
Fluency effect	If a claim sounds smooth or is visually polished, it feels truer.	A glossy ROI slide makes the causal link feel credible.
Confirmation bias	We favor data supporting what we already believe about cause and effect.	A buyer highlights outcomes that validate choosing your solution.

Sales mapping:

•Availability → cherry-picked case studies.

•Illusory causation → dashboard narratives (“pipeline grew after feature rollout”).

•Fluency → slick demo narratives that oversimplify.

•Confirmation → misread pilot results that match expectations.

Even skilled analysts fall prey: once causality feels intuitive, disconfirming evidence gets discounted.

General Language Cues

•“Since we started X, Y improved.”

•“After introducing A, B happened.”

•“Once we stopped doing that, results got better.”

•“The only change was…” (implying singular cause)

Visual/Structural Cues

•Before-after charts without control data

•Dashboards showing sequential upticks as “proof”

•Narratives that skip intervening factors or time lags

Sales-Specific Cues

•ROI calculators that equate correlation with causation

•Deck slides labeled “Results after switching” without attribution model

•Buyer objections like “We tried your competitor and churn dropped—so theirs must work better”

•Reps saying: “Customers who added this module always see faster growth.”

Spotting these patterns early allows you to steer the discussion toward evidence, not assumption.

Examples Across Contexts

Context	Claim	Why It’s Fallacious	Stronger Version
Public discourse/speech	“Crime rose after the mayor took office, so their policies caused it.”	Correlation in time doesn’t prove policy effect; other social factors may drive change.	“Let’s examine crime trends before and after, adjusting for regional factors.”
Marketing/product/UX	“Engagement spiked after redesign—so users love it.”	Could stem from novelty or campaign traffic, not design quality.	“A/B testing shows engagement persisted across cohorts; design likely contributed.”
Workplace/analytics	“Revenue fell after remote work started, so remote work caused decline.”	Many confounds—market conditions, product mix, etc.	“Regression analysis shows only partial correlation; other variables explain variance.”
Sales scenario	“After our pilot, the client’s conversion rate doubled—proof our platform drives sales.”	Could reflect seasonality, ad spend, or external campaigns.	“Conversion rose post-pilot; we’ll validate causality with a controlled comparison.”

How to Counter the Fallacy (Respectfully)

Step-by-Step Rebuttal Playbook

1.Surface the structure: “So we’re saying because X came before Y, X caused Y?”

2.Clarify burden of proof: “Can we check if other factors changed at the same time?”

3.Request missing premise: “Do we have data ruling out coincidence or external drivers?”

4.Offer charitable reconstruction: “Maybe X contributed indirectly—let’s test that.”

5.Present valid alternative: “Let’s design a simple control to see if the effect holds.”

Reusable Counter-Moves

•“Sequence doesn’t equal cause—what else could explain the shift?”

•“Let’s separate correlation from attribution.”

•“Could this be timing rather than causality?”

•“Would a control or baseline help us confirm that link?”

•“Let’s test before scaling the claim.”

Sales Scripts

•Discovery: “I hear that performance improved after using that tool—was anything else changing at the time?”

•Demo: “Our clients saw these outcomes, and we validate causality through pilot benchmarks, not just timing.”

•Negotiation: “I’d love to ensure the ROI model reflects cause, not just coincidence—shall we review assumptions together?”

•Renewal: “Retention rose after rollout, but we measure against matched cohorts to confirm the driver.”

•Objection handling: “That’s a good observation—timing may suggest a link; let’s verify it’s causal before deciding.”

Avoid Committing It Yourself

Drafting Checklist

When asserting a cause, check:

•Scope of claim: “always,” “because of,” “led to”

•Evidence type: anecdote vs. controlled data

•Warrant: logical connection supported by mechanism?

•Counter-case: could the opposite happen?

•Uncertainty language: “may have contributed,” “appears linked,” “correlated with”

Sales Guardrails

•Phrase benefits as potential, not guaranteed (“tends to improve,” not “will double”).

•Cite mechanism + proof point, not sequence (“reduces manual steps, which can improve throughput”).

•When unsure, defer: “We can validate in a limited pilot.”

Before/After Sales Argument:

	Weak (Fallacious)	Strong (Valid/Sound)
Claim	“After installing our analytics tool, client X grew 40%, so our tool causes growth.”	“Client X’s growth coincided with analytics adoption; to test causality, we ran matched comparisons showing data-driven optimization contributed to 10–15% of the lift.”

Table: Quick Reference

Pattern / Template	Typical Language Cues	Root Bias / Mechanism	Counter-Move	Better Alternative
Sequential cause	“After we did X, Y happened.”	Illusory causation	Ask for confounds	Run controlled test
Correlated success story	“Clients who used this feature grew faster.”	Availability bias	Request baseline data	Cite cohort analysis
Competitive framing	“After switching vendors, performance improved.”	Confirmation bias	Ask what else changed	Compare matched periods
ROI claim inflation	“Our customers double ROI after onboarding.”	Fluency effect	Separate timing from cause	Phrase as potential impact
Urgency pitch	“Teams that delayed missed targets.”	Loss aversion heuristic	Reframe around evidence	Offer timeline scenarios

Measurement & Review

Lightweight Audit Tools

•Peer review prompt: “Does this claim assume cause from sequence?”

•Logic linting checklist: Identify every “after,” “since,” or “led to” phrase.

•Comprehension check: Ask a colleague to restate the causal logic—does it still hold?

Sales Metrics to Monitor

•Win rate vs. deal health: Are promises based on genuine evidence?

•Objection trends: Are buyers questioning your ROI logic?

•Pilot-to-contract conversion: Are pilots validating causal claims?

•Churn patterns: Is overselling causality leading to disillusionment?

For Analytics & Causal Claims

•Use basic experimental design: control group, randomization, and adequate sample size.

•Watch for confounds: seasonality, spend shifts, policy changes.

•Remember: this article is about reasoning quality, not legal proof.

Adjacent & Nested Patterns

Common co-occurrences:

•Post Hoc + Straw Man: Misstating a competitor’s result, then attacking it.

•Post Hoc + Ad Hominem: Blaming a person for timing coincidences (“since she joined, metrics fell”).

Sales boundary conditions:

•A legitimate budget freeze may coincide with deal loss—temporal link, but not fallacious if supported by evidence.

•Example: “After budget cuts, fewer renewals occurred”—plausible cause if independently confirmed.

Conclusion

Post Hoc Ergo Propter Hoc is seductive because it feels neat: sequence appears to imply cause, stories fit, and dashboards glow green. Yet reasoning this way distorts truth and undermines credibility.

The antidote is not cynicism but discipline: test causal links, use humble language, and welcome verification.

Sales closer: Causal clarity builds trust. When buyers sense rigor instead of rhetoric, forecasts improve, renewals rise, and relationships last.

End Matter

Checklist: Do / Avoid

•Validate causal links in ROI slides.

•Ask what else changed before claiming impact.

•Use comparative or controlled evidence.

•Phrase outcomes probabilistically (“tends to improve”).

•Invite buyer scrutiny of assumptions.

•Cross-check timing vs. attribution in analytics.

•Train reps to flag causal language in decks.

•Audit success stories for logical structure.

Avoid

•Saying “after” as proof of “because.”

•Over-claiming causation in discovery calls.

•Treating case studies as universal laws.

•Ignoring alternate explanations.

•Equating correlation with proof.

•Selling outcomes you haven’t verified.

•Dismissing objections about data quality.

Mini-Quiz

Which of these contains Post Hoc Ergo Propter Hoc?

1.“Leads rose after our new CRM—so it caused the increase.” ✅

2.“Leads rose after our CRM, but we’ll test if it was the main driver.”

3.“Leads rose and fall seasonally; the CRM effect is unclear.”

References

•Copi, I. M., Cohen, C., & McMahon, K. (2016). Introduction to Logic (14th ed.). Pearson.**

•Walton, D. (2015). Informal Logic: A Pragmatic Approach (2nd ed.). Cambridge University Press.

•Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.

•McRaney, D. (2012). You Are Not So Smart. Gotham Books.