Spencer and Jeff expose how the brain misinterprets randomness, turning messy data into false patterns—like numerology’s meaningless letter counts or QAnon’s anagram "evidence" (e.g., 25-letter strings mimicking one-handed typing). A book falsely linked Lewis Carroll to Jack the Ripper via cherry-picked anagrams, while conspiracy theorists ignore context to force significance onto outliers. Even Spencer’s fixation on "25" in phrases reveals arbitrary bias, proving most coincidences lack real meaning. The episode underscores how pattern recognition without rigor fuels baseless theories and wastes mental energy. [Automatically generated summary]
And we're back with Truth Unrestricted, the podcast that would have a better name if they weren't all taken.
I'm Spencer, your host, here today again with Jeff.
How are you doing, Jeff?
Not too bad, buddy.
How about you?
Good.
So, as always, at the top of the episode here, we remind everyone where they can send any feedback.
That's truthunrestricted at gmail.com.
Let us know if you think we got anything wrong or if there's anything we missed.
You think we should have added something in or not mentioned something.
Whatever you think you'd like to tell us about the show, send that email there.
And today, we want to talk about false pattern recognition and coincidence.
These two really kind of slide in together with each other, but it is a lot of words when you put them all together.
So, everyone takes math class, right?
In like elementary school and high school, and you have these problems, math problems, and you're told what you're meant to find, and then you're given all the numbers that you need to find it.
And in almost every case, you also only have just the numbers you need, you don't have any additional numbers, and you have to arrange the numbers in an equation somehow to come up with the answer.
And you know, you have an answer that you're looking for.
And basic, basic arithmetic and algebra, usually there's only one way to get to that answer with the handful of numbers you've been given.
Well, sometimes, sometimes there's other things you could do that come up with the wrong answer or what have you, right?
But you're generally given exactly the things you need, and then you are told to find the answer.
And then there's one answer to find also, which is mostly true in most of high school math.
There's only one answer, which is not always true once you get to math that occurs after high school, by the way.
And this is a sort of a thing you experience for most of your mathematical life.
And for most people in most situations who go through high school, that's that also accounts for all the logic you'll ever learn at that level, is whatever you learned in math.
And this leads you to this sort of practiced thing about what to do when you have a problem.
You have all the pieces, and then you have the solution, and you put the pieces together, and then you have a solution.
But it doesn't always work that way in real life.
In fact, it never really works that way in real life.
All real life problems are far messier than the simple pat problems you would get in like a high school math class.
So, false pattern recognition occurs when you have a large number of data points or facts or observations, and you're trying to find patterns.
Intelligence, one of the primary factors in measuring intelligence is the ability to find patterns.
And your brain is always trying to find patterns, it's always working on patterns.
But you will find a lot of situations where you have many more data points than you really need, and your brain will pick out some of them and find a pattern in there.
And it just won't really be a real pattern.
I mean, this is exactly how numerology works as a thing.
I'm sorry if there's any numerologists out there that were really hoping I was going to tell them that what they were doing was they were on the right track, but you're just not.
It's false pattern recognition.
And it's a numerology is actually a system that's set to set you on a path of finding the wrong answers for nearly everything.
Did I say nearly?
Really, I meant everything.
Pretty much everything.
Yeah.
You might accidentally find the right answer, but really it's only accidental.
So does that make sense so far?
Yep, I think so.
Great.
Like, like the idea is, you know, tying in with the broader subject matter that we usually discuss, like conspiracy theorists would find like seeking to find proof, proof of the activities of the Illuminati would find patterns in the activities of the stock market to prove some dastardly plan.
Sure.
They might look for little bits and find it everywhere.
There's obviously other types of logical fallacy that can help them along the way.
And this is just one of them.
But it's definitely a common one that you see a lot.
And we don't talk about a lot.
So I want to talk about it here.
So right alongside this, I want to talk about statistics because we use statistics an awful lot.
We quote statistics.
It's been talked about a lot, the way people will just pull statistics out of nowhere.
And just, you know, 80% of statistics you see online are made up on the spot.
And by the way, I just made up that statistic on the slide.
So you see how easy it is to just grab numbers.
Everyone understands how percent works.
So it's easy to just say, you know, slur a percent in there.
And that's pretty much the right number.
There's a part of statistical analysis, a thing called an outlier.
Now, when you have, I'm trying to not get too deep in the weeds here on, you know, all the many factors in statistical analysis, but it's been pretty common in many places now that there are outliers.
And this is a common thing.
Most people don't understand what a standard deviation is.
That's a very elementary part of statistical analysis is a standard deviation.
And most people don't know what it is and don't care.
Everyone knows what an outlier is, pretty much, which is an interesting thing about our world.
So an outlier is a piece of data that doesn't appear to fit well with the rest of the data set that it comes with.
You're going to measure the time it takes for an ambulance to respond to a call.
And you're going to have hundreds of different examples of ambulances responding to calls.
And you'll put them all on a scale and they're all between 10 minutes to 45 minutes or something.
And then you have one that's like two and a half hours.
And you'll just be wondering, what happened there?
Why was one of these two and a half hours?
Like, what's going on?
So outliers.
Sorry to interject.
And I don't know what where this plays on where you're heading with this.
But like in that specific example you gave, one of the problem with this with the statistical outlier would be if we just looked at those times and averaged them and we're like, oh my God, ambulance response times are so much longer.
And I'm not going to throw the math off the top of my head, but like the one heavy, heavy, heavy long trip could drag the data down so far that it on average, it looks like they're all taking too long.
And then, if we're acting on vague statistical analysis, then perhaps we might try and perform some across-the-board upgrade to all ambulance stations to improve efficiency.
You know, supercharge the engines at vast expense of every ambulance, when really it's just one random trip to one remote location that almost never happens.
Well, that might have been what happened with that particular outlier in that example, right?
That's a possibility.
But we see outliers like people who have no formal training in statistics look at statistical analysis as though it's some kind of magical device to come up with the answer you're looking for.
Because there have been a lot of people who have misused mathematical principles to manufacture statistics that they are looking for.
And most people think that what they're doing is they're just removing data points that are called outliers.
If you want your ambulance data to look good, you could get rid of the two and a half hour one, but you could also call some of the other ones that are on the high side, also outliers, and just remove them in theory and say that our average response time is under 20 minutes or something.
It's really good and we don't need to spend any money.
But, you know, and people look at statistics as though this is probably happening, especially when they see a statistical result that they don't like.
They'll immediately think, oh, well, you know, these statistics, you can mangle these numbers into positions too.
They can say anything you want, can't you?
You know, statistics is just one of these out of lie with numbers.
Yeah, it's one of these gray area mathematical disciplines that allows you to say whatever you want in the end, right?
Which isn't true at all.
I hope someday to get a proper statistical, mathematical person on the podcast to talk about many, many things without, you know, bogging the thing down in unreasonable terms like standard deviation, for example.
But for now, you just have to deal with me who's had some training in how statistics comes together.
And I'll tell you that outliers are things that in any set of data, the outliers will tell you something.
They are all significant.
They'll all tell you, each one individually will tell you something if you look into the matter.
Sometimes they only tell you about how reliable your measurement of your data is.
So it could have been that the person who was supposed to keep track of the times for the ambulances just forgot to write the time down when they were done and then wrote down a different time and it worked out to two and a half hours and because he was just sloppy.
That could have happened.
You don't know that until you look into the matter.
You can't just dismiss it out of hand.
And that's true with statistical analysis: you can't just choose to remove data from the pool arbitrarily.
You have to have a reason to remove an outlier from the data pool, or if you're going to remove more than one.
So in keeping with the example, let's say the one two and a half hour call, the reason why it was a two and a half hour call is because there were two in a larger township, there were two ambulance bays, like dispatch halls.
And one of them, the bus was already out on a call, and another call came in in their coverage area, and the other ambulance had to cross boundaries to attend.
So it was actually a cross-zone call, which might remove it from the study group when we're talking about how quickly ambulances respond to calls within their coverage area.
Right.
If, and that's that's a situational thing that you have to, that you have to make a decision on about your data set.
If you're only interested in the data about in your coverage zone, then that's not part of your data and you can really remove that.
If there's some other reason, that might even that other reason might still give you a reason to remove it, but you have to justify removing it.
Yeah.
You have to have a reason.
And when we look at false pattern recognition, in almost every case, you are removing most of the data in order to see the little bit of a pattern that you're looking at.
I mean, I'm talking about, it's possible.
Fun thing that younger people don't realize is that there used to be a thing called white static on your TV.
Old analog televisions, when you tuned it to a channel that didn't have a signal, it would just come up with white noise.
It was actually white and black spots all over the place.
And it had a fuzz sound.
Right.
And if you stared at that for long enough and focused right on it, you could start to see shapes in there.
I mean, this was the age before the internet when, you know, on a Saturday morning after the cartoons were done, we didn't have anything else to do.
Sometimes we would try other stuff.
And every once in a while, we would, you know, that'd be on the wrong channel and you'd look at it because you're trying to avoid, you know, your going outside.
Going outside or whatever.
And kids do this sort of thing, but you can see patterns in there, shapes.
Yeah.
And that's those shapes aren't really there.
It's just an artifact of your brain attempting to find patterns where none exist.
And that's a perfect example.
Faces in clouds is another perfect example of this effect that your brain is actually engineered to find faces and recognize features of a face.
And so when you stare at clouds and you see faces in the clouds, that's your brain looking for a pattern that doesn't really exist.
The cloud isn't really making a face.
It just has shapes, and sometimes your brain interprets those as the shape of a face.
And you're exactly right that when people come up with conspiracy hypotheses, they do this an awful lot.
They'll look at very selected things.
And when you're looking at people who are attempting to sell you on the idea that the thing they came up with or the thing they heard from someone on the internet was this is real, you know, and they look at there's all kinds of these examples.
There's, there's a large number of people who were of Jewish descent who canceled their flights the night before 9-11.
And it turns out that there were also a large number of people of Christian descent who canceled their flights on the night before 9-11.
And there was people who were Muslim who canceled their flights and everything.
There was an awful lot of people who were set to fly who canceled their flight on the night before 9-11.
There really was a lot of people who did cancel their flight.
And then other people who got on those flights because they were on standby.
This is a thing that's happening every day.
And the fact that it happened on that particular day is of no note whatsoever because it's still happening even today.
Other things like, you know, your neighbor's cousin had a health problem three days after he got vaccinated or something.
And also, your, you know, some other distant relation or even someone at work, someone you know or something also had some other thing that was within some timeframe, somewhat close to a vaccination or what have you.
And you put all these things together and they go, well, you know, maybe these vaccines are a problem.
Yeah.
But those are very, like, even if you had a couple of them.
Those together are not that many compared to the number of problems that might have happened when the vast majority of us actually got vaccinated.
Yeah, it's like most drug commercials, right?
You know, the running gag is always like the list of potential side effects is longer than the pitch.
Right.
But like 100% of, oh, listen to me, quoting percentages, all of those side effects are in themselves statistical outliers or the drug would not be released for market.
But we have to hear about them because that's the law.
Yeah.
But like those kinds of outliers, particularly in medicine, you're always going to get those kinds of outliers because human physiology and human biology is very frigging complicated.
And there's always a lot of factors in play, especially when it comes to drug interactions and vaccine interactions.
So yes, these things are always going to happen.
But like, yeah, like you say, if you if you cherry pick the handful of things where you've heard that it happened and it's so much easier for us as a people to get access to a larger swath of statistically not insignificant, but a statistical minority of data, we can get access to all of those individual data points so much more easily thanks to the internet.
Oh, yeah.
So you can go for it, like a cast a wide net and come up with all of these anecdotal stories of vaccine injuries, but like chalk it up against the literally millions upon millions upon millions of I got my shot and then I didn't get sick.
Yeah.
So one hilarious example of this is sometime in the 90s, there was a person who wrote a book.
I can't actually find the name of the book, but I do have a link to an article that was written about the book.
This person was attempting to say that Lewis Carroll, Lewis Carroll being the author of several pieces of children's literature.
Including Alice in Wonderland.
Yes.
Yeah.
Yes.
That Lewis Carroll was actually Jack the Ripper.
Oh, I heard this one.
Yeah.
Right, right.
That he lived around the same time and was from England.
Both useful facts if you're going to look for someone who was actually Jack the Ripper.
Jack the Ripper.
Yeah.
And the only other bit of evidence.
But to be fair, a low bar to set, there were lots of people living in that time in England.
The book had another bit of delightful thing, which was that Lewis Carroll was a big fan of anagrams.
Anagrams are when you take a phrase and you rearrange all the letters in the phrase to make a different phrase.
Right.
So this person was taking anagrams from pieces of Lewis Carroll's literature to show other things that could be made from those anagrams, other sentences that could be made from those anagrams that showed some kind of evil intent.
So they were looking for anagrams in Lewis Carroll's literature.
Right.
Say other things like Niener, Niener, Niener, I'm Jack the Ripper.
Well, I mean, I don't have all the quotes from the book that show this, but in this article, it did show one of the quotes that was anagrammed.
It was from Jabberwocky.
Jabberwocky does have some strange words in it.
T'was brillig and the slithy toves did Gyrin gimbal in the wabe.
All mimsy were the burrow groves and the moam wraths outgrabe.
I think that's the first two lines from Jabberwocky.
That is absolutely the first two lines.
That, you know, two lines together, anagram into, check this out, bet I beat my glands till with hand sword I slay the evil gender.
A slimy theme, burrow gloves, and masturbate the hog more.
Very clever, very clever anagram.
Yes.
Yes.
A bit of a reach.
But the person who did this article turned one around on the author of this book, and he took an entire paragraph from this book, and then he anagrammed that into another thing entirely.
So I'll read that out too.
The paragraph from the book is, this is my story of Jack the Ripper, the man behind Britain's worst unsolved murders.
It is a story that points to the unlikeliest of suspects, a man who wrote children's stories.
That man is Charles Dodgson, better known as Lewis Carroll, author of such beloved books as Alice in Wonderland.
So that's that's the paragraph from it anagrams into the truth is this.
I, Richard Wallace, stabbed and killed a muted Nicole Brown in cold blood, severing her throat with my trusty Shiv's strokes.
I set up Oranthel James Simpson, who was utterly innocent of this murder.
P.S.
I also wrote Shakespeare's sonnets and a lot of Francis Bacon's works too.
There is no way you get that out of that.
Well, I haven't actually checked it.
I haven't actually taken them.
Well, I will check that.
I'll put them into a thing and rearrange them and see if it actually comes to that.
I'm calling for fact check.
You're not allowed to air this until you fact-check that.
But if it's true, if it's true, that is awesome.
It is amazing.
Yeah.
Editing Spencer here to tell you that I checked the anagram in question and it checks out.
It is exactly right.
Thank you.
And back to the show.
There's another one that's very interesting and probably closer to our current age of conspiracism.
It comes from actual QAnon.
Now, for those who aren't really savvy on how QAnon worked, everyone is kind of aware that it's a series, like a collection of conspiracies that are all working together.
And there's something about, you know, Trump's a good guy somehow.
And, you know, he's going to save the world.
But Q was a series of messages that were posted on a 4chan message board.
Eventually it got moved to 8chan and then got kicked off there and moved somewhere else.
And that's not really that important to this story.
The messages that were there included always sort of a with the messages were included a series of numbers and letters.
And this was a big part of the what a lot of people who were in that space were looking at was what these numbers and letters meant.
And they were always looking for ways to analyze them and ways to pick them apart and ways to see what they meant.
And some people would pick out some numbers and arrange them and say, oh, well, maybe these are coordinates for somewhere on the planet, or maybe these are this or that.
Maybe it's a year or something, right?
Maybe it's a date.
Like almost turning it into a mysticism, like it's almost got a misogyny quality.
Yeah, a lot of a lot of things like that.
And of course, they weren't using all the letters and numbers in each message.
So immediately they're false pattern recognition immediately.
They're not even coming close to using all of them.
They're cherry-picking the numbers they want, putting them together in a way that they prefer.
And there doesn't seem to be any rhyme or reason to exactly what their analysis system needs to be.
Seriously?
So like out of like a literal shitpile of random letters and numbers, they only pick up a handful and cook a conspiracy with that.
Well, the messages were also like at least with anagrams, we used up all the letters, like work for it, people.
Well, I mean, the messages, I mean, the messages that were coming were also leading people to like this idea that there was a person in the White House who was very close to Trump and was working with Trump to, you know, expose the deep state and arrest all the people who were attempting to subvert control of the nation and blah, blah, blah.
And I mean, these were all part of the messages, but these extra string of numbers and letters were always included as a part of the message.
And everyone thought that those were somehow an additional coded message and they'd try to find different parts of that code.
So there was a person who did analyses of different things, different things.
This guy, he actually had done an analysis of passwords that had been, they were, I don't know where he got the passwords from exactly.
Maybe they had been from different hacker groups that had hacked things or whatever, but he just took the bulk passwords in general and he analyzed them all and he did things like showed which passwords were used all the time and which ones were better and did a lot of work for things like improve your passwords kind of thing.
So this guy, as an additional thing, he looked at the QAnon phenomenon and he decided that he would do an analysis on all of these phrases.
These seemingly, I mean, in the minds of the people who listened to QAnon and were really hoping it was leading them to save the world, that, you know, to those people, it wasn't random, but to him, maybe, you know, he wasn't sure.
So he took them and he analyzed them and he, you know, had a whole list of them and he came up with the analysis.
And in his analysis, he did an analysis that I have to find it here.
I think it's called a heat map, which is essentially, I mean, if you overlay the numbers and letters on a keyboard, you can, first of all, with each individual string, you can light up the keys for the ones that are in that string.
And then you can also apply like a warmth color to the numbers that are used more often across all of them.
Right.
So he first found right away that for each individual string of numbers and letters, they were essentially random, but they were always on like one side of the keyboard, either the left side or the right side.
So it was, it was done just as if a person put their hand on the keyboard on, you know, the left side of the right side, where you would normally have your hand if you're going to do real typing.
And you just selected random keys to hit with that hand held right over that side of the keyboard and then hit send, right?
And this is the analysis that he found.
He, you know, which is just obvious, what, I mean, that's exactly what you would expect, right?
You would expect to have a random, from my perspective anyway, you'd expect to have some randomness to this, such that, you know, it's just a person coming up with random numbers and letters.
Yeah.
And I think that's important to point out to people who are deep in this space is that you're not dealing with a mastermind that's attempting to lead you to something.
You're dealing with a person that's attempting to mimic a mastermind that's leading you to something.
And they're providing pseudo data for you to follow really to nothing.
So to put it in layman's terms, a guy who's really good at statistical math figured out that these letters and numbers that are being dumped in QAnon without a doubt are the result of one hand randomly slapping the shit out of one side of the keyboard.
That's yeah, that's right.
Yeah.
I love it.
Yeah.
And he he also immediately put his results on a website and he challenged people to show him what he's wrong or provide their own analyses.
And it's important to remember that these are just bullshit.
All of these complicated, overly complicated schemes are based on almost nothing.
And in fact, with QAnon, they're based on a person that's trying to lead you to a specific conclusion based on no information at all.
And that's a, I don't know how to say that in more succinct terms.
With something like Flat Earth, well, they just kind of have a penchant for the Bible, I think, mostly, and what they think is important from what they read in the Bible.
And probably more importantly, that they think that the science should be wrong somehow.
But with QAnon, there was a specific goal that this person who was putting these messages up wanted from their audience.
There was a result they were going for.
And when you went for that result, when you gave them that result, I mean, you were following along lockstep.
But it's terribly mysterious.
Yeah, but they call people sheeple.
But as soon as you're following that guy, he's the Pied Piper.
And you're just following right along with him.
Like you're the sheeple.
I don't know how to tell you that in more stark terms.
Well, like particularly with the anagram thing, too, right?
Like there's a reason why it's considered a game for the brain.
Because like human language being what it is, if you take a pool of words, they reduce to their base components of letters is just a good sampling of random letters.
And you can then decide, okay, what words do I want to make?
And you, at the beginning of the process, if the sentence is of any length, you're going to be able to find the raw materials you need to make whatever words you want.
Yeah.
And then the challenge becomes as you work your way deeper in and the raw material gets fewer and far between.
You have to get more creative.
But particularly if you're willing to like completely ignore sentence structure and syntax, like that hilarious one that you offered from Jabberwocky.
Like, yeah, like it's, it's, it's quite easy to come up with any collection of words that says whatever the hell you want to say.
So like you can't invest anything in that from a conspiracy standpoint at all.
So I need to share something that my brain does that is false pattern recognition.
And I, I've been doing this.
My brain has kind of been doing this in its spare moments when I'm not focusing on other more important things.
It just kind of does this.
My brain will take phrases that I've heard, maybe phrases from songs or from podcasts I'm listening to, or even phrases from books I'm reading.
If I'm not really, really engrossed in the book and I just kind of, you know, half into it, it'll, it'll be doing this in the background.
It'll take phrases and it will count the letters in them.
It'll just count them.
It doesn't count them up to a total.
It just picks individual phrases and counts them and then picks another phrase and counts that one and picks another phrase and counts that one.
And then guess what?
I get happy when it comes up with the number 25.
It's this funny thing that I used to do when I was younger too, to a much greater extent, actually.
For a while in my life, after high school and before I went back to school, my brain was doing this to nearly everything that I heard in every conversation and every sign that I passed everywhere.
And for a while, I was pointing it out to people whenever there was the number 25 showing up and people were just like weirded out and everything else.
But of course, I wasn't only coming up with the number 25, I was also coming up with nearly every other number that was reasonable length.
I was almost never coming up with the number eight because I was never looking at phrases that were that short, but it was very often coming up with 23 and 24 and 28 and 32.
Because that was your target.
But my brain only lit up whenever there was a 25.
And I'd only point it out to people whenever there was a 25.
So it would make it look like there was a lot more phrases that were coming out to a length of 25 letters than any other length.
And of course, it was completely untrue.
Even I knew this, even as my brain was doing it, I knew it was untrue.
But I mean, I still do it now.
My brain still kind of does this now when I'm just not really doing anything else.
It just needs things to do.
So I just do this.
I've started playing an inane game when I spend a lot more time driving for work now.
I'm on the road right now.
But like anytime I'm on the highway and there's dashed lines visible, like I just, I have to count them.
And I pick a multiple and I see, hey, let's see if the guy who painted this line, this, this section of dashed lines, let's see if he made it in multiples of three.
And you have a one in three chance of getting it on the mark, right?
Yeah, exactly.
It was a slightly less chance for getting, you know, a sequence with 25 letters in them.
Yeah.
But still, my brain was just wholesale ignoring every result that it, you know, that was in the number 25 and only highlighting the ones that were 25.
And that's, you know, maybe that's a sign of my future psychosis, but it's there.
It's just a thing my brain has done a lot of, and it just continues to do.
I don't make anagrams.
I count letters.
And it's useless.
And I prefer to work on things, you know, I prefer to set my brain to tasks that are more useful, but I don't always have useful things to put it to.
So it does this instead.
But it's, if I were to become a person who was going to try to assign meaning to this, I could easily be exactly like many of the people we see who are conspiracists.
I could say that 25 is the key.
It's five squared and it's a magic number.
It's got all this symmetry to it.
And it's, you know, you could really go down a rabbit hole with it.
It doesn't mean anything at all.
It's just a number.
And that's important to remember is that most of these things that are happening aren't happening because they're special.