Math represents itself symbolically, and can best be described as the science of structure and order, which represents as patterns across our perception. Unlike other sciences, though, repetition doesn’t prove a mathematical concept: the only way we can tell is with deductive certainty that it can’t be anything else.

Math is the science of order itself, but represents as a distinct language built around clearly defined values.

Structurally, math is grounded in logic, with zero room for uncertainty. Even uncertain things are clarified on *how* uncertain they are.

Math is grouping logical values we’ve interpreted. The form of “3” may exist in some ethereal plane (like Plato had once thought), but our human structure links objects together into patterns to say there’s “3 apples” or “3 cars” or “3 letters”.

Technically, math only exists in the mind. It’s an advanced overlap of logic that sits on top of what we see as reality to help us understand it more clearly.

Most people who highly value math have a difficult time with the mind-based location of math, simply because its reliability within nature makes them believe it’s immaculate. Within most STEM and accounting, math *is* reality and not merely in our minds.

## Useful

Math is incredibly useful for us to find patterns in the world around us. It’s the ultimate means of attaining order.

Numerical values are useful to achieve and track outward results. We always form a *non*-numeric purpose before we start using numbers:

- Someone desires to lose weight and be thin (mostly non-numeric), so they measure the 10 kg they want to lose (numeric).
- A scientist wishes to understand how black holes interact with space (non-numeric), so they measure light bending from surrounding stars (numeric).
- A CEO aims to know how well a company is doing (non-numeric), so they run reports that measure different parts of the company (numeric).

The numbers we use are simply relational to other numbers. If someone attained 4, then later attained 6, someone would consider that an improvement, but that would change if you knew they were expected to attain 793. This is a *major* tactic for deception, especially when we anchor to the first number we hear.

Living the good life requires us to *avoid* using numbers to find purpose or meaning. By quantifying anything, we define our feelings by an added abstraction from reality instead of reality itself.

In any meaningful situation, the ultimate goal of a numerical measurement will be a non-numerical purpose. Any measurement that is also a higher purpose on its own is either performing another’s non-numerical desires or veering into a form of addiction.

## Types

There are many classes of math, and far too many to easily specify, but they all start with the primitives of arithmetic and algebra.

There are too many to specify, but the branches of math naturally expand proportional to the needs of the people calculating with them. This means that math (and its pedagogy) branch off into many, *many* specializations.

However, on a highly advanced level, math has two broad purposes of analysis:

- Reproducing the elements of reality with numbers, which is largely the domain of most math disciplines like calculus and game theory.
- Capturing reality itself with numbers to observe any trends that may emerge, which is all derived from statistics.

## Statistics Uses

Most reality yields statistical results, so statistics has a vast range of applications. It can track how far someone can throw a ball, a rocket’s likely trajectory, radioactive chemicals’ half-life, and social trends. It can also often show how we change habits, behave in groups, and make decisions.

Normally, we interpret certainty in our minds through convictions grounded in experiences. Statistical systems, however, create an anomaly to that mode of reasoning:

- Gathering statistics requires heaping up stories, but only for specific and measurable elements of those many, many stories, without any consideration of other things that aren’t being measured.
- All analysis derived from the results can’t naturally regard the qualitative experiences that were trimmed during collection.
- Since we all need stories to understand reality, most statisticians easily interpret cause-and-effect through correlation.

Statistics are tracking reality, and reality is uncertain, so all statistics are always a little uncertain. They’re alarmingly accurate (when measured correctly) at determining correlation, but only in a broad sense.

Statistics, however, can *only* demonstrate correlation. Any causation only comes through how the information is interpreted:

- People in the 1980s who ate hot breakfasts more often than cold were dramatically more likely to have Alzheimer’s Disease. We tend to eat what we ate when we were kids, and cold cereal took off in popularity in the 1950s.
- Crime statistics
*always*go up when cities expand their police forces. Technically, crime statistics are only measuring*caught*crimes. - The culture of most of the world aligns with anthropology studies of the West, representative by data. Most anthropology data is gathered near Western universities.
- People who saw the 1984 Ghostbusters movie are more likely to die than people who saw the 2021 Ghostbusters movie. This is simply because of age-related facts.

When we hear a statistical result, we’re incapable of *not* inserting our explanation on the causes or effects of the information. Our only hope to gain full understanding lies in forced uncertainty, which requires directing our thoughts toward the specific purpose of suspending judgment, which requires tremendous effort to maintain.

Statistics don’t fit our intuition for a few core reasons:

- When there are different sample sizes from different groups (e.g., populations of various districts in a country), the less-populated groups are
*guaranteed*to have more high/low extremes, simply from having fewer numbers. - Observing statistical anomalies (e.g., very unusual height) creates more engaging stories and spurs the imagination, and it requires training to
*not*feel the outliers more than the norm. - Possessing information on a chart doesn’t explain anything directly. It indicates a connection, but that connection has to stay unknown for us to be accurate, and we
*hate*not knowing. - When we experience the possibility of a risk, we only feel comfortable when its chances become 0%. For that reason, we tend to obsess about the smaller risks (which can theoretically be eliminated) at the opportunity cost of managing the larger ones.
- Over time, statistical things trend back to normal from extremes (e.g., high-performing athletes, extremely depressed people). Without measuring what normally happens, we can imagine that a statistical change was caused
*by*a measured element included in it.

## Algorithms

An algorithm is a math problem where the formula has specific rules that only sometimes apply (e.g., X-5=Y if X is more than 5, but otherwise X=Y).

An algorithm is subject to the same realities as statistics: the incoming variables determine its accuracy to reality.

However, hiding the actual algorithmic calculation is a very effective way to distort how things appear. The people who run most computer technologies over a network can often tweak information for various reasons from what it legitimately was expressing.

## Application

Since math is in our minds, it has the same fickle properties as any other value. However, since it uses logic so intimately, we can structure it very well by comparison to any other values.

Math is *very* useful in many parts of life, but its value is based on the accuracy of the math user’s mind. Further, the signal deteriorates as it’s communicated, as well as whether they’re trustworthy and honest, and is augmented by how they’ve calibrated their feelings.

Math is a logical symbol, so enough inputs and outputs into a mathematical proof is guaranteed to yield itself as offensive, inaccurate, confusing, or inappropriate.

To solely value money is to assign a measurement of power as power itself.

We can never expect something to go *precisely* how we envision it because we always gain 1 sample of a statistical range at any given time. All we can do is change our statistical likelihoods whenever possible.

Math is useful, but it’s always comparative logic. We must keep in mind what *any* number is comparing itself to, such as percentages or statistics.

A statistical report is a statement of curated observations and is as trustworthy as the group it came from. There’s lots of money and power if someone can prove they’re not biased (which isn’t possible), so the people who create statistics often have something to lose by stating that they’re not precisely accurate.

Very frequently, statistics hide lies by making the statistician appear more credible. The value of the statistical analysis comes from the value of the raw data, which comes from the quality of the data collection.

Claiming statistical analysis is immaculate requires faith in a community’s collective understanding. Nothing is all/nothing, but statistics provide the opportunity to make a clear story of reality, even if it’s not precise. However, most younger people can be influenced to believe statistics more than its truth.

Since math exists in the mind alone, algorithms are always biased, no matter how much data is fed into it or how well it’s constructed, meaning algorithms are as fallible as statistics.