Definitions by Abzugal Nammugal Enkigal
Scientific Picking
The deliberate or institutionalized practice within scientific research of selecting only hypotheses, experimental designs, data, or analyses that are likely to yield a preferred, publishable, or fundable result. This includes p-hacking, HARKing (Hypothesizing After Results are Known), and the file drawer problem (not publishing null results). It corrupts the scientific process by making the literature a curated museum of "successes," not an accurate map of reality.
Scientific Picking *Example: A pharmaceutical company runs 20 trials on a new drug. The two that show a mild positive effect (likely by chance) are published. The 18 showing no effect or harm are filed away. This Scientific Picking creates a public, peer-reviewed "fact" of the drug's efficacy that is a complete statistical mirage.*
Scientific Picking by Abzugal Nammugal Enkigal February 4, 2026
Cognitive Biases of Cognitive Biases
The specific, recursive set of errors we make when trying to identify, label, and correct cognitive biases. This includes: Bias Attribution Bias (attributing others' actions to their biases, but your own to circumstances), Fallacy Fallacy applied to biases (dismissing someone's point because you spotted a bias, even if their point is valid), and the "I'm Educated on Biases" Bias (assuming knowledge of bias lists makes you immune to them).
Cognitive Biases of Cognitive Biases Example: You accuse a friend of confirmation bias for only reading news that aligns with her politics. She retorts that your accusation is itself driven by fundamental attribution error (a Cognitive Bias). You then dismiss this as a tu quoque fallacy (a Fallacy Fallacy). This infinite regress of bias accusations is the hall of mirrors created by Cognitive Biases of Cognitive Biases.
Cognitive Biases of Cognitive Biases by Abzugal Nammugal Enkigal February 4, 2026
Metalogical Metabiases
Biases in how we think about metalogical choices and the very criteria we use to judge logical systems. It's bias two levels up. For example, valuing aesthetic elegance or psychological comfort over practical utility when deciding which logical framework to adopt for describing the world. It's the irrational driver behind your rational choice of rationality tools.
Metalogical Metabiases Example: A physicist prefers string theory over loop quantum gravity not due to empirical data (there is none), but because of a Metalogical Metabias: they find its mathematical beauty and conceptual unity more compelling. The bias is in the meta-criterion ("beauty") used to choose between competing metalogical frameworks for quantum gravity.
Metalogical Metabiases by Abzugal Nammugal Enkigal February 4, 2026
Metalogical Biases
Prejudices that operate at the level of metalogic—the study of the properties of logical systems themselves (like consistency, completeness, soundness). A metalogical bias might be an irrational attachment to classical logic as the "One True Logic," rejecting non-classical systems (like paraconsistent logic that tolerates contradiction) because they feel wrong or threatening, not because they are unsound for certain problems.
Metalogical Biases Example: A mathematician has a metalogical bias for completeness. They deeply distrust any proposed logical system that is proven to be inherently incomplete (like Gödel showed for arithmetic), viewing it as "broken," even if it's incredibly useful for computer science or legal reasoning where paradoxes must be managed.
Metalogical Biases by Abzugal Nammugal Enkigal February 4, 2026
Logical Metabiases
Biases in how we select, apply, and trust different systems of logic themselves. This is a bias about your philosophical toolbox. For instance, a preference for crisp, binary logic (true/false) in situations requiring fuzzy or probabilistic reasoning, or the bias of dismissing an entire line of argument because it uses a logical framework (e.g., dialectics, abduction) you're not comfortable with.
Logical Metabiases Example: An engineer, steeped in deterministic, Boolean logic, dismisses a sociologist's dialectical analysis of social change as "illogical." This is a Logical Metabias. The engineer is biased against a whole form of reasoning appropriate for complex, contradictory systems, falsely believing their own logical paradigm is universally supreme.
Logical Metabiases by Abzugal Nammugal Enkigal February 4, 2026
Logical Biases
Systematic patterns of deviation from norm or rationality in the application of logical rules, often driven by emotion, worldview, or cognitive shortcuts. This isn't about formal fallacies, but about the biased choices we make within logic: which premises we accept, which inferences we draw, and which counter-arguments we entertain. It's the subjectivity hidden inside the objective shell of logic.
Logical Biases Example: Two people see the same data on tax cuts. One, with a pro-market logical bias, immediately infers it will stimulate investment. The other, with an equity-focused logical bias, infers it will increase inequality. The same logical tool (inference from data) is wielded to different ends based on prior ideological commitments.
Logical Biases by Abzugal Nammugal Enkigal February 4, 2026
Metacognitive Biases
Flaws in our self-monitoring and self-regulation of thinking processes (metacognition). These biases distort our judgment of our own understanding, learning, and problem-solving abilities. Key examples include the Dunning-Kruger effect (poor performers overestimate their ability) and the Illusion of Explanatory Depth (believing you understand something complex until you have to explain it). They are biases in the "dashboard readings" of your own mind.
Metacognitive Biases Example: A student crams for an exam and feels a strong "feeling of knowing." This Metacognitive Bias leads them to stop studying, confident they've mastered the material. During the test, they blank—their metacognitive gauge of knowledge was faulty, mistaking familiarity for understanding.
Metacognitive Biases by Abzugal Nammugal Enkigal February 4, 2026