Logical Fallacies Non Sequitur: 10 Evidence-Based Coaching Practices
Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Logical Fallacies Non Sequitur, these are the strongest matches in the current practice library.
Practices that may help
- The Ludic Fallacy: When You Mistake Real Life for a Game
The ludic fallacy, named by Nassim Taleb in The Black Swan, is the mistake of applying the logic of well-defined games (known rules, bounded outcomes, stable probabilities) to domains where those assumptions do not hold — most of real life. The fallacy matters because standard risk models built on game-like distributions systematically underestimate the frequency and magnitude of extreme, unexpected events. This is Taleb’s analytical concept; the supporting evidence is largely observational and historical rather than from controlled experiments. - The Narrative Fallacy: Why We Can’t Stop Making Stories
The narrative fallacy, named by Nassim Taleb in The Black Swan, is the tendency to construct coherent, causal stories from sequences of events — and then to mistake the story for the underlying reality. Narratives feel explanatory because they are causal and emotionally coherent, but they systematically underweight randomness, omit disconfirming details, and create false predictability. The bias is real and well-grounded theoretically; its practical importance is high wherever decisions depend on forecasting or learning from the past. - Explicitly distinguish coherence from probability
A story can be internally consistent and still be rare — learn to separate "makes sense" from "likely."
The Conjunction Fallacy — When "More Details" Feels More Likely - Test each component probability separately
Before judging a joint claim, estimate each element on its own, then check whether the conjunction is lower.
The Conjunction Fallacy — When "More Details" Feels More Likely - Question whether the category you’re reasoning from actually fits
Before applying a model or framework, verify that the category it was built on genuinely matches your situation.
The Ludic Fallacy: When You Mistake Real Life for a Game - Separate the argument from the arguer
Address the claim on its merits, not the person’s motives or character.
Productive Disagreement, Made Practical - Identify what the narrative leaves out
After hearing or constructing a causal story, deliberately list the facts it doesn’t explain.
The Narrative Fallacy: Why We Can’t Stop Making Stories - Anchor on base rates before adding details
Start with how common the outcome is in the relevant population, then update — don’t start with the story.
The Conjunction Fallacy — When "More Details" Feels More Likely - Recognize that random sequences don’t "owe" balance
Random processes have no memory — a run of heads doesn’t make tails more likely.
The Representativeness Heuristic — Judging by Resemblance - Weigh disconfirming evidence more heavily than confirming evidence
One strong counterexample outweighs many confirming examples for a universal claim.
Falsification Thinking
Does this sound like the set of challenges you might be facing?
- Because I can explain exactly why something would happen, I treat it as likely to happen
- When two things fit together neatly in my head, I treat the whole package as more likely than either piece alone
- I’m reaching for a framework that worked great somewhere else and dropping it onto this situation, and a part of me isn’t sure the two cases are actually the same kind of thing underneath
- I notice I’m really arguing against this person
- I’ve got this clean, satisfying explanation for why it all went the way it did, and it feels airtight
Related concerns
- Logic Fallacy Freshman
The narrative fallacy, named by Nassim Taleb in The Black Swan, is the tendency to construct coherent, causal stories from sequences of events — and then to mistake the story for the underlying reality. Narratives feel explanatory because they are causal and emotionally coherent, but they systematically underweight randomness, omit disconfirming details, and create false predictability. The bias is real and well-grounded theoretically; its practical importance is high wherever decisions depend on forecasting or learning from the past.
- Logical Fallacies Cherry Picking
Before applying a model or framework, verify that the category it was built on genuinely matches your situation.
Question whether the category you’re reasoning from actually fits
- Logical Fallacies Generalization
Before applying a model or framework, verify that the category it was built on genuinely matches your situation.
- Logical Fallacies 11 Minutes
The narrative fallacy, named by Nassim Taleb in The Black Swan, is the tendency to construct coherent, causal stories from sequences of events — and then to mistake the story for the underlying reality. Narratives feel explanatory because they are causal and emotionally coherent, but they systematically underweight randomness, omit disconfirming details, and create false predictability. The bias is real and well-grounded theoretically; its practical importance is high wherever decisions depend on forecasting or learning from the past.
- Logical Fallacies Appeal To Ignorance
The ludic fallacy, named by Nassim Taleb in The Black Swan, is the mistake of applying the logic of well-defined games (known rules, bounded outcomes, stable probabilities) to domains where those assumptions do not hold — most of real life. The fallacy matters because standard risk models built on game-like distributions systematically underestimate the frequency and magnitude of extreme, unexpected events. This is Taleb’s analytical concept; the supporting evidence is largely observational and historical rather than from controlled experiments.
- Logical Fallacies Appeal To Tradition
The narrative fallacy, named by Nassim Taleb in The Black Swan, is the tendency to construct coherent, causal stories from sequences of events — and then to mistake the story for the underlying reality. Narratives feel explanatory because they are causal and emotionally coherent, but they systematically underweight randomness, omit disconfirming details, and create false predictability. The bias is real and well-grounded theoretically; its practical importance is high wherever decisions depend on forecasting or learning from the past.
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