## bookmark_borderThere Is a Fundamental Flaw in How We Do Statistics in Science

Suppose I tell you that only 1% of people with COVID have a body temperature less than 97°. If you take someone’s temperature and measure less than 97°, what is the probability that they have COVID? If your answer is 1% you have committed the conditional probability fallacy and you have essentially done what researchers do whenever they use p-values. In reality, these inverse probabilities (i.e., probability of having COVID if you have low temperature and probability of low temperature if you have COVID) are not the same.

To put it plain and simple: in practically every situation that people use statistical significance, they commit the conditional probability fallacy.

When I first realized this it hit me like a ton of bricks. P-value testing is everywhere in research; it’s hard to find a paper without it. I knew of many criticisms of using p-values, but this problem was far more serious than anything I had heard of. The issue is not that people misuse or misinterpret p-values. It’s something deeper that strikes at the core of p-value hypothesis testing.

This flaw has been raised in the literature over and over again. But most researchers just don’t seem to know about it. I find it astonishing that a rationally flawed method continues to dominate science, medicine, and other disciplines that pride themselves in championing reason and rationality.

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## bookmark_borderCan a finite physical device be Turing-equivalent?

If you believe in the following, I am going to try to change your mind:

“Turing machines aren’t realistic. They need infinite memory so they can’t be implemented. Any real computing device is limited in its memory capacity and, therefore, equivalent to a finite state machine.”

This is a fairly commonly held view. I used to believe in it myself, but had always found it deeply unsatisfying. After all, modern-day computers have limited memory capacity but closely resemble Turing machines. And Turing’s abstract formulation arguably led to the digital revolution of the 20th century. How, then, can the Turing machine be a physically irrelevant mathematical abstraction? If all of our computers and devices are of the weaker class of computers, namely, finite state machines, why do they have to look so much like Turing machines?

It is important to clarify this, especially for neuroscience and computational biology. If we think of Turing-equivalence as this abstract level of computation that is impossible to physically achieve, then we block out classical insights from the theory of computation and cannot even begin to ask the right questions. (I recently wrote a manuscript asking the question “where is life’s Turing-equivalent computer?” and showed that a set of plausible molecular operations on RNA molecules is sufficient to achieve Turing-equivalent computation in biology).

It took a good amount of reading and thinking to finally understand the meaning of Turing-equivalence. I will explain what I believe to be the only consistent way of looking at this issue. Here is the short version:

When we say Turing machines require “unbounded memory”, what we mean is that memory cannot be bounded by the systems descriptor, not that it cannot be bounded by other things such as the laws of physics or resource constraints. Turing-equivalence only requires a system in which memory usage grows, not one in which memory is infinite.

Below I explain precisely what all that means. I will try to convince you that this is the only consistent way of looking at this and that Turing, himself, shared this perspective.

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