Data is not the same as a model. A sample is a finite bag of observations; a population is the story you wish the bag came from. Almost every formula later in this course is a map from samples to guesses about populations.
| Kind | Examples | Typical summary |
|---|---|---|
| Categorical (nominal) | color, ticker sector | counts, proportions, bar chart |
References and further study
Each source below is selected for this course. Use the study note to connect it to the lesson, then cite the original source in submitted work.
Copyright rule: MatheLinux links to the official source and stores only the citation. It does not copy or host external books, papers, videos, or datasets. Learners must quote sparingly, cite the source, and follow the source's own licence or terms.
Submit work
Before you continue
Lesson close · Elementary Statistics
Use this checkpoint to recall the lesson before marking it complete. The concepts below come from this lesson's authored sections.
Stuck? Send a blocker
One sentence on what stopped you (definition, derivation, lab control, time). This feeds Module-1 product metrics — not a public comment thread.
Progress
Sequence
Continue to Discrete Distributions I, the next prerequisite step in this course.
| Categorical (ordinal) | rating 1–5 | same + order-aware medians |
| Numeric discrete | count of defaults | histogram of integers |
| Numeric continuous | return, latency | histogram / density, mean & sd |
Randomness lives in the sampling process, not in “the number looking noisy.”
Always look: histogram / bar chart / scatter. Outliers, multimodality, and truncation show up visually long before they show up in .
Slow pass: definitions, one derivation, two numeric checks, then the exam failure mode.
Write the governing identity. Specialize to a 2-variable or n=2 case so arithmetic stays honest. Check a boundary (p=0, det=0, n=1) before trusting the general formula.
Change one parameter from the first example. Predict the direction of change, then compute. If the prediction fails, fix the story before software.
Interactive · sample size intuition
rough SE scale ~ 1/√n = 0.224
Submit work
Optional human feedback—not required to mark the lesson complete. Pick a brief, submit it, or continue without submitting.
Human-graded work is a Pro course feature
The free lesson, quiz, and problem checks remain available. Pro members can choose a brief below, submit work, and receive a score and written feedback.
Previous submissions
No submitted work for this lesson yet.
