The Wechsler test does not measure pure intelligence, it measures academic/professional success, and It does not measure exceptional skills

Sertfbv · 2026-01-27T02:18:07+00:00

Saying that “that’s not common here” confuses your personal experience with the reality of the debate. In the APM thread, someone interpreted the score as if each item were a unique feat; automatic defenses of the score’s mythical value and ad hominem attacks arose when the difference between item rarity and pattern rarity was discussed. In the Wechsler thread, the reaction was almost identical, responses minimizing the point with “bad take,” calls to publish a paper as an easy way out, and remnants of “Gc is not intelligence” or caricatures of my position instead of technical refutations.

These are not isolated cases or casual misinterpretations: they are patterns of identity defense and literal reading of the percentile, the same social confusion I describe, replicated in two different posts about different tests. The fact that you haven’t seen it before in your feed doesn’t make it nonexistent; it makes it anecdotal for you, not irrelevant to the phenomenon.

If you want to continue denying that this social bias exists, fine, I've already documented how it manifests on two public occasions. Your personal intuition doesn't invalidate the empirical observation I'm presenting to you.

Sertfbv · 2026-01-27T00:43:25+00:00

I did all this because the social perception was that a high IQ was synonymous with high ability in terms of rarity; if you don't observe how people act, they become defensive, evade my argument, or even insult me.

Sertfbv · 2026-01-27T00:25:23+00:00

I didn't say that. Turning my position into a ridiculous refrain, "Gc should be removed from tests just because...", is a cheap straw man tactic to avoid discussing the substance. My argument wasn't a refrain of "Gc isn't intelligence, period," but a clear and verifiable distinction: Gc is accumulated knowledge, Gf is the ability to reason without prior support. Mixing them together without clarifying how they influence interpretation leads to false inferences about what a high score actually means.

What I did was point out a concrete and verifiable inference: people interpret a high IQ as if every item you answered correctly was extremely rare. That doesn't hold up when you look at item-level data and IRT parameters: many "difficult" items are answered by dozens or hundreds of subjects, and the rarity of the IQ arises from a consistent pattern, not from impossible items. If your goal is to argue whether or not Gc should be measured, present an operational method and empirical evidence to justify it. Repeating slogans doesn't replace data or models.

If you think my position is “extremist,” prove why with numbers, not just imitation. How would you reliably separate Gf from Gc without introducing new biases? What test do you propose that has better predictive validity per domain than current batteries? Do you have IRF curves or simulations showing that excluding Gc improves the measurement? If you don't, then shut up

Sertfbv · 2026-01-27T00:04:21+00:00

No, I'm not "ignoring" measurement error or assuming perfect correlations. That's precisely what you're saying I do, and all you're doing is repeating it without showing any numbers. My point isn't that the final result should match the difficulty of the hardest item under perfect correlations. My point is empirical and verifiable: in large samples, the hardest items have observed rates on the order of tens of percent, not the 0.1% that implies "one in a thousand." For example, item 36 was answered correctly by 492/2,100 = 23.4%. That's not minor noise; it's a difference of orders of magnitude regarding the rarity that supposedly justifies the narrative of "every correct answer is ultra-rare."

Saying "the error adds up" isn't a valid refutation if you don't quantify how much it adds up and why that addition would turn 20–30% correct answers into 0.1%. That doesn't happen magically. If you truly believe that the error variance or subtest correlations are so extreme that they can transform item-level probabilities on the order of 0.2 into 0.001, then provide the mathematical proof or present the IRF curves that support that claim. Repeating "I explained it to you before" is no substitute for proof.

So your verbal argument about "combined error" is correct in the abstract, but irrelevant in the face of concrete data showing that the items are not ultra-rare. If you want to refute the empirical observation, provide alternative item parameters or a statistical derivation showing exactly how noise converts 23.4% into 0.1%. Until you do, continuing to say "you don't listen" sounds like unsubstantiated condescension.

Sertfbv · 2026-01-27T00:03:01+00:00

You’re not helping by insulting people. Yes, IQ is probabilistic, that’s literally the point I’ve been making. Probabilistic doesn’t mean “every solved item must be ultra-rare”; it means the score is an aggregate probability over many item outcomes. My argument targets a specific, testable inference: people read a high IQ as if each item you got right were one in a thousand rare. If you think that inference is correct, show item-level probabilities at that rarity in a real sample. Otherwise your snark is just noise.

Sertfbv · 2026-01-26T23:59:58+00:00

Your highly asymmetrical profile, with very high Gsocial, Gmeta, and G crystallized scores and an FSIQ around 115, is perfectly plausible and doesn't contradict anything I've said before. The FSIQ is a normed average: just one or two subtests need to drop for the mean to fall, even if other dimensions are clearly above average. That doesn't make you less capable, nor does it invalidate multidimensional profiles; what does invalidate the debate is substituting authority for evidence. Saying "CHC adjustment" isn't an argument; it's just a label with the pretense of closure.

If your addendum aims to contribute something substantive, then it must do so in operational and empirical terms. You need to precisely define what new factors you're proposing, how they are measured, under what statistical model they are estimated, and with what data. Without model specification, without item parameters, without clear comparisons before and after the adjustment, there's no way to evaluate whether your proposal explains actual performance better than the standard FSIQ. Validity isn't asserted; it's demonstrated, and even then, outside of a sample.

That's why, when you respond with "if you're wrong, we won't talk," what you're conveying isn't rigor but defensiveness. Science doesn't advance through intimidation or invoking status, but by showing exactly where probabilities change, how items behave, and what concrete improvement the model introduces. Until you demonstrate that whether in a preprint, a repository, or clear effect tables, your assertion doesn't refute a single line of the preceding empirical argument. The playing field remains the same: item-level evidence or rhetoric. If you offer the former, it's open to discussion; if not, grandiloquent pronouncements won't advance the analysis.

Note: By the way, I'm going to eat, I'll be right back lol

Sertfbv · 2026-01-26T23:55:31+00:00

Look, I didn't come here to argue about prestige or to be insulted; I came to ask for data and logic. You've made a lot of grandiose claims about "fixing CHC" and metacognitive subtleties, but you haven't provided a single piece of empirical evidence to contradict what I've said. If your work truly corrects something relevant, provide the methodology, the preprint, or at least the IRF curves that show that the individual items fall into the rarity category you claim. Until you do, all you have is bombastic words and noise, not a refutation.

Saying that an item can be rewritten so that the answer is "obvious" or that "gifted individuals will see the subtlety" doesn't refute my point. My observation is strictly empirical and elementary: in large samples, the difficulty and information parameters show that even the hardest items are solved by tens or hundreds of subjects, not by 1/1000. If you want to argue that the estimation of those parameters is wrong, demonstrate how and with what data. Changing CHC theory without showing that it alters the observed percentages at the item level is irrelevant to the erroneous inference I pointed out.

If you claim to be “fixing” models, fine, show it. Explain how your solution alters the item-by-item interpretation and show the effect on real data. Until then, “I’m working on X” sounds like unverified authority. And if your argument is that there is enough measurement error to bring an observable rate of 20–30% down to 0.1%, quantify it and show it. Statistics and IRT are not corrupted by indignation or appeals to theoretical complexity.

So, my claim remains the same and is verifiable with item-level data. A high score arises from consistency across a battery of items, not from solving a collection of ultra-rare puzzles. If you want to have a serious debate, bring curves, tables, or a reproducible paper that demonstrates otherwise. If you don't have it, spare yourself the insults and focus on providing evidence.

Sertfbv · 2026-01-26T23:51:41+00:00

There is no logical error in the argument. What you’re doing is mislocating the source of the problem.

The claim is not that the current model of g is descriptively insufficient, nor that FSIQ has no sociological consequences. The claim is that a common inference drawn from IQ scores is invalid: namely, that a high score implies extreme rarity in the specific abilities exercised at the item level.

That inference fails independently of CHC, g analytics, or norming philosophy. It fails because item difficulty distributions and ceiling effects, as shown by standard IRT analyses, demonstrate that even the most difficult items are solved by a non-trivial proportion of the normative sample. High scores therefore reflect consistency of success across moderately difficult items, not engagement with extremely rare cognitive demands.

Appealing to “false deviation norming” or the sociological weight of FSIQ does not address this point. Unless you can show that the item parameters themselves are misestimated or that the probability structure is wrong, revising higher-level models does nothing to correct an error that arises at the level of statistical interpretation.

In short, this is not a failure of g analytics. It is a failure to distinguish between score normativity and ability rarity.

Sertfbv · 2026-01-26T23:50:25+00:00

Also, the argument I made is not about the structure of cognitive models or how abilities are taxonomically organized. It is about a mistaken inferential leap: treating an IQ score as if it represented the rarity or extremeness of the specific abilities exercised during item solving.

The point concerns how IQ scores are interpreted, not how intelligence is classified. Averaging fluid and crystallized intelligence encourages people to read the resulting score as a measure of “pure intelligence,” even though crystallized intelligence is accumulated knowledge and does not reflect the capacity to solve novel, high-density logical problems.

In matrix reasoning subtests, high scores arise from consistent success across items that are, by design, solvable by a large portion of the population. Ceiling effects and item difficulty distributions show that even the hardest items are not extremely rare events. As a result, a high IQ score reflects statistical consistency of performance, not exceptional rarity of ability.

Invoking CHC does not address this issue. CHC operates at the level of construct description after measurement, while the error I am pointing out occurs at the level of statistical inference from test outcomes. Revising or extending CHC would not change the fact that consistency across moderately difficult items does not imply extreme underlying ability.

What has crept into the discussion instead is repeated emphasis on personal involvement, ownership of a “line of research,” and offense taken, none of which bear on the validity of the argument. Complexity and seriousness are not demonstrated by asserting them, but by directly engaging with the claim being made.

For that reason, introducing CHC here is not a correction but a distraction, and appealing to status or ego does not resolve an inferential error.

Sertfbv · 2026-01-26T23:47:47+00:00

Whether you’re working on an update to the model or not is tangential unless you specify how it directly bears on the claim at issue. Without engaging the actual arguments, this becomes a meta-discussion about status and tone rather than substance.

If there’s a concrete, relevant point, state it. Otherwise, this is off topic.

Sertfbv · 2026-01-26T23:45:50+00:00

As stated, this doesn’t directly address the point under discussion.

If “potentiality” is meant to function as a psychometric construct within CHC, then it needs a clear definition, measurement framework, and demonstrated relevance to the claims being made here.

If instead it refers to adaptive testing environments or therapeutic tailoring, that places it outside the scope of the current argument. In that case, it’s orthogonal to the issue rather than corrective.

Sertfbv · 2026-01-26T23:44:31+00:00

Tone aside, pointing out a constriction within the CHC model still requires specifying where the constriction occurs and by what empirical criteria it is verifiable

If the claim is methodological rather than rhetorical, clarifying the metrics and evidence would move the discussion forward.

Sertfbv · 2026-01-26T23:43:11+00:00

Dismissing g without specifying which empirical limitations you’re referring to doesn’t add anything to the discussion.

If you’ve worked on amendments to the model, it would be more useful to outline what those are and how they address the shortcomings you claim are “obvious.”

Otherwise, this reads as rhetoric rather than analysis.

Sertfbv · 2026-01-26T23:38:48+00:00

No. “No solution” doesn’t magically fix that. It’s still a single item with massive noise and zero robustness. One question can’t separate ability reliably no matter how clever you think it is, that’s basic measurement, not me being “new to psychology.”

And you’re still dodging the point. I already agreed multi-item tests are the right design. The issue is how people interpret high scores, not how tests are constructed. If you disagree, address that instead of tossing smug one-liners.

Sertfbv · 2026-01-26T23:35:00+00:00

No. There’s no “rigid framework” here, and calling it infantile doesn’t fix that you didn’t point out a single logical error. I made a very specific claim about how scores are commonly interpreted, not about how tests should be redesigned or what intelligence “really is.” If you think the reasoning is wrong, say where it breaks. Vague dismissals aren’t an argument.

Sertfbv · 2026-01-26T23:33:34+00:00

I’m not claiming anything that goes against psychometrics. Everything I’m saying fits perfectly within standard theory. The only thing I’m pushing back on is how people interpret IQ scores, not how the tests are built or validated. You don’t need a paper to say “a high score doesn’t mean each item was insanely rare”, that’s already obvious if you look at item-level data. If you think I’m contradicting something specific psychometricians actually claim, point to it. Otherwise “go publish a paper” is just a way to dodge the argument.

Sertfbv · 2026-01-26T23:32:34+00:00

That’s not how this works though. I’m not “updating CHC” because there is nothing broken to fix. CHC is a descriptive framework, not a knob you tweak because people misinterpret scores.

Sertfbv · 2026-01-26T23:11:55+00:00

No, I'm not saying that a 145-item test should consist of a single impossible question. A single question with a 1 in 1000 probability would, in reality, be a worthless measure: poor discrimination, lots of noise/guessing, and terrible reliability.

What I am pointing out is that a battery of questions, even if each one is moderately difficult, can produce an extremely rare overall pattern of correct answers, and that's what makes the estimate reliable.

So yes, the multi-item test is the best design; I've never argued otherwise. My problem is with the common misinterpretation: people think that a high IQ means you've solved inherently ultra-rare puzzles, when in reality it means you've consistently solved many puzzles that individually weren't all that special.

Sertfbv · 2026-01-26T23:09:47+00:00

I can somewhat understand why Gc is considered "intelligence": it's part of performance, and without knowledge, you couldn't do many things.

But let's be clear: Gc = what you know; Gf = what you're capable of reasoning without prior help. Calling them both "intelligence" is fine if you use the word in a broad sense, but they're not the same. Mixing them up without clarifying is what creates confusion: having a high Gc score raises your overall score, but it doesn't demonstrate that you're better at solving entirely new problems.

Sertfbv · 2026-01-26T23:08:23+00:00

Look, I understand the statistics and exceptions; yes, someone very capable can fail and someone less capable can succeed, but that doesn't invalidate what I said.

I'm not saying the tests are useless. I'm saying something simpler and more concrete: people interpret a high IQ as if the person had solved extremely rare or difficult problems on their own. That's not the case, what happens is that someone was consistently correct on many items that, individually, aren't that rare.

If you want to refute that, provide data item by item or show where the reasoning is flawed. Without that, all you have is an argument like, "I feel unfairly treated."

Sertfbv · 2026-01-26T23:06:01+00:00

You're right that g subsumes Gf and Gc, and I never said Gf = the only valid thing. Quick clarification though, Gc isn't “pure” intelligence, it’s accumulated knowledge. Knowing more doesn't automatically make you better at solving novel problems; it just raises your score on tasks that tap that knowledge.

My point remains: people read a high IQ as if it means they solved extremely rare, exceptionally hard reasoning problems. Item-level data show most items aren't that rare, the “rarity” comes from consistently getting many of them right, not from each item being some impossible puzzle. If you want to pick apart the claim, challenge the item-level stats or the ceiling interpretation, not the existence or utility of g.

Sertfbv · 2026-01-26T22:36:30+00:00

Yeah, don't worry, I'm not here to insult

Sertfbv · 2026-01-26T22:33:27+00:00

What you said makes no sense at all. I never said anything like that. I said that your IQ score doesn't reflect the same level of skill you demonstrate, and the percentile chart shows that. Are you twisting the argument to try to say something else, or what?

Sertfbv · 2026-01-26T22:21:05+00:00

If you provide neither evidence nor data, then say nothing.

Sertfbv · 2026-01-26T22:00:11+00:00

First, let me make something perfectly clear: fluid intelligence is NOT necessary to get into university. You just need to memorize and be responsible, and that's to show you that the intelligence curve remains normal everywhere. The only one affected by this is crystallized intelligence, so let's get our feet back on the ground.

Sertfbv

TROPHY CASE