The topics we cover, and the myths attached to each

Why organise a site around myths rather than definitions

Most explanations of information processes start with a diagram of boxes and arrows and expect the reader to absorb the vocabulary before anything useful happens. That approach works for someone already halfway to understanding the subject. It does very little for someone who has only heard secondhand claims about what a model does or does not prove.

We chose the opposite starting point. Each topic page opens with a belief people commonly hold, tests it against how models are actually built and used, and only then introduces the terminology needed to explain the correction. The vocabulary arrives once it is needed, not before.

This is not a stylistic choice made for entertainment value. Errors about modelling tend to cluster around a small number of recurring misconceptions, and naming them directly is a more efficient way to prevent them than presenting a complete framework and hoping the reader infers the exceptions.

Collection: what counts as an input, and what does not

The collection stage of any information process determines which raw material a model will ever see. A common myth is that a model works with everything relevant to a situation. In practice a model only receives what its inputs are defined to capture, and anything outside that definition is invisible to it regardless of how important it might be in the real situation being represented.

Our page on collection walks through everyday examples, such as a household budgeting spreadsheet or a weather forecast, to show how the choice of what gets recorded shapes every later stage. It also addresses the belief that more data collected automatically means a more accurate model, which is not something that follows without further conditions being met.

Processing: what a model does with what it is given

Processing is the stage most associated with the word model in everyday speech, and it is also the stage most misunderstood. A widespread myth treats processing as a neutral, almost mechanical step that simply reveals what was already true in the data. The page on processing explains why every processing step embeds assumptions, and why changing those assumptions changes the output even when the input data stays identical.

We use plain examples such as a recipe scaled up for a larger group, or a simple average used to summarise a set of measurements, to show how processing choices that look small can move a result more than the underlying data does.

Storage: what persists, what degrades, and what disappears

Storage is often treated as an afterthought, a place where information sits unchanged until it is needed again. The myth here is that storage is passive. In reality, storage decisions determine what format information survives in, how long it is kept, and whether later processes can even access it in a usable form.

Our storage page covers ordinary situations, such as an old letter kept in a drawer versus a digital file on an unsupported format, to illustrate that storage is an active part of an information process with its own rules and its own failure modes, not a neutral pause between other stages.

Transmission: what changes when information moves

Transmission covers how information travels from one point to another, whether that is a conversation passed along a chain of people or a message sent across a network. A common myth is that transmission is a copying operation, where what arrives is identical to what was sent. The page on transmission addresses delay, loss, and distortion as ordinary features of transmission rather than rare faults.

Everyday examples such as a rumour changing shape as it passes between people, or a photocopy of a photocopy losing sharpness, make the underlying point concrete without requiring any technical background from the reader.

Assumptions and limitations: the part most often skipped

Perhaps the most persistent myth about any model is that a well-built one has no real limitations, only rough edges to be smoothed out with enough effort. Every model rests on assumptions that are chosen, not discovered, and every model has a scope beyond which its outputs stop being meaningful.

This page treats assumptions and limitations as a normal, expected feature of modelling rather than a weakness to be apologised for. Readers leave with a way of asking what a given model assumes and where it is meant to stop applying, which is often the missing question in public discussion of models.

Relationships between inputs and outputs

A separate page focuses on how inputs and outputs relate to one another once collection, processing, storage and transmission are all in play. The relevant myth is that a model's output is a direct, one-to-one reflection of its input. In practice the relationship is mediated by every choice made at the earlier stages, so the same input can produce different outputs depending on decisions the reader may never see.

We use household and everyday scenarios throughout, deliberately avoiding specialised or commercial examples, so the reasoning transfers to whatever situation the reader actually cares about.