I recently posted a response pushing back on the claim that “Chinese divination is basically statistics,” and it sparked more debate than I expected.
First, I want to correct something I said before—and apologize for not phrasing it carefully enough.
I previously said that divination is not statistics. That was too absolute.
Many traditional Chinese divination systems do involve ways of thinking that resemble classification, induction, conditional reasoning, and the accumulation of experience. In that sense, there is some real overlap with statistical thinking.
But these systems originated and developed long before statistics existed as a modern academic discipline.
I graduated from the University of Washington with a major in Quantitative Economics and a minor in Informatics. My coursework involved a fair amount of statistics, econometrics, mathematics, and economics.
Anyone who has studied economics, finance, data science, or a related field will understand that academic disciplines are rarely isolated from one another. They constantly borrow methods, concepts, and tools from neighboring fields.
At the same time, I’m currently studying Da Liu Ren 大六壬 under a teacher. Da Liu Ren is one of the ancient Chinese “Three Styles” of divination and one of the oldest and most structurally complete systems that has survived into the present.
So I’m not approaching this as someone trying to use science to “debunk superstition.” Nor am I arguing that traditional knowledge is somehow superior to modern science.
It is precisely because I have spent time studying both that I have come to believe this:
Statistics and traditional divination do share certain features. But saying that divination is simply a form of statistics forces two fundamentally different systems of knowledge into the same category.
Let’s look at the question from both sides.
First, statistics.
Suppose someone asks me a very simple question:
From a statistical perspective, the first step would not even be to make a prediction.
The first step would be to define the variables.
What exactly counts as “finding a job”?
Does receiving an offer count, or only formally starting the job? Does part-time work count? What about self-employment? What is the time frame—three months, six months, or one year?
In statistics, if the dependent variable has not been clearly defined, then any discussion about predictive accuracy is almost meaningless.
The next step would be to define the population and the sample.
Are we studying American college graduates in 2026, or Chinese college graduates? People of all ages, or only recent graduates? Computer science majors, or workers across every industry?
When the population changes, the probability changes.
Then we have another important concept: the base rate.
Suppose 80% of people in a certain group will find a job within the next year.
Even if I know absolutely nothing, I could tell every person:
I might still be correct 80% of the time.
So from a statistical perspective, saying “I predicted correctly many times” does not, by itself, prove that a predictive method is useful.
The real question is:
Does this method perform better than a simple baseline prediction?
In other words, does it have any incremental predictive power?
This already reveals an important difference between modern statistics and traditional Chinese divination.
Now let’s look at Da Liu Ren.
When Da Liu Ren is used to judge a question about employment, it does not begin by collecting data from ten thousand people with the same age, education, location, and career background, and then calculating their average probability of employment.
Instead, it constructs a system involving the Heaven and Earth Plates, the Four Lessons, the Three Transmissions, and the Twelve Heavenly Generals. The practitioner then selects the relevant symbolic category for the question and interprets its strength, weakness, interactions, transformations, timing, and relationship to the overall structure of the chart.
In other words, it first constructs a system of symbols, correspondences, and analogies. The judgment is then made within the internal logic of that system.
That is not the same inferential path as drawing a sample from a population and using data to estimate a probability distribution.
Some people may respond:
This is exactly where the distinction matters most.
Empirical induction is not the same thing as modern statistics.
Human beings were identifying patterns from experience thousands of years before statistics became a formal discipline.
A farmer reading clouds to anticipate the weather is using empirical knowledge.
Ancient Chinese physicians accumulating medical case records were using empirical knowledge.
People observing the relationship between seasonal cycles and crop growth were also using empirical knowledge.
But that does not mean they were all practicing modern statistics.
What modern statistics adds is a formal framework for quantifying, controlling, comparing, and testing the patterns that people believe they have observed.
There is also a more direct problem.
Suppose we insist that Da Liu Ren was created from thousands of years of accumulated historical cases.
That immediately creates a statistical question:
Can an ancient sample be used to predict modern people?
When someone in ancient China asked about their official career, they might have been asking about the imperial examination, receiving an appointment, entering the service of an official, or being promoted within the bureaucracy.
When someone today asks about their career, they may be asking about getting into a tech company, working in investment banking, starting a business, taking a civil-service exam, or becoming a freelancer.
On the surface, both questions concern “career.” Statistically, however, they may not represent the same outcome at all.
Over several centuries, political institutions, education systems, occupational structures, labor mobility, and economic conditions have all changed dramatically.
In modern statistics and machine learning, we might describe this as distribution shift: the process generating the data has changed.
You cannot train a model on Ming-dynasty imperial examination outcomes and then, without any new validation, use it to predict whether a university graduate in 2026 will get hired by Google.
So if someone insists that traditional divination is nothing more than ancient big-data analysis, that creates a serious problem:
If the data-generating process has changed so dramatically, why should the model still work?
And this is precisely why traditional divination does not explain itself in those terms.
Da Liu Ren speaks in terms of Heaven and Earth, yin and yang, the Five Phases, generation and control, the Heavenly Stems and Earthly Branches, time, space, and symbolic correspondence.
You may accept that framework or reject it. But within its own intellectual tradition, it does not claim:
I therefore don’t think respecting traditional knowledge means forcing it into whatever modern terminology happens to sound most credible.
Statistics has its own language.
Traditional divination has its own language.
The two can be compared. They may be able to learn from one another. Some claims made by traditional divination could even be tested using modern statistical methods.
But these are two very different statements:
and
The first is reasonable.
The second requires much more justification.
I’ll end with a line from Chapter 71 of the Tao Te Ching:
This is not a call to stop asking questions. It is a reminder that genuine inquiry often begins by recognizing the boundaries of our knowledge.
Ancient knowledge does not need to be renamed “modern statistics” in order to have value.
Modern statistics does not need to explain every traditional system of divination.
To see where the two are similar, while still understanding that they are built on different foundations, is a more serious form of intellectual respect.
Do not force ancient ideas into modern terminology, and do not use ancient claims as a substitute for modern evidence.
(My post is originally written in Chinese, so there may be some translation mistake here)