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Data, probability and statistics I · Lesson 10 / 12

Together in the data — but why?

Route A appears faster during one shift. It may be better; it may have received lighter deliveries. An observed difference does not explain its own origin.

Learning paths18 min

Let's discover together

  1. 1

    Association describes how variables appear related in records. Causality claims that changing a condition changes an outcome under defined circumstances. Simply observing different groups does not establish that claim.

  2. 2

    Load weight, distance and shift may affect duration and route choice. They are alternative explanations to investigate. Do not turn them into proven causes without evidence; list what remains unknown.

  3. 3

    In a fictional experiment, define the outcome measure in advance and randomly assign comparable deliveries to conditions. This helps reduce systematic group differences; small samples may still be unbalanced. Repeat and record deviations from the plan.

  4. 4

    Randomly selecting a sample and randomly assigning each case to a condition serve different purposes. The first concerns population selection; the second concerns comparison design. Keep this exercise on paper or in simulation, without experimenting on people or real hazards.

Evidence lab

Experiment with fictional deliveries at Aurora station. Analysis runs in this browser. Nothing entered here is sent to the server.

The workshop resets when you leave or reload. Download data and report to keep your work, or send the report to the activity editor, which keeps a draft in this browser. Completion and XP belong to the account.

Header id,group,minutes; one delivery per row; unique id; groups A/B; decimal point; duration 0–1440 with up to two decimal places. Blank means unknown. Up to 100 rows. No quotes or decimal commas. Use invented data.

Selection uses the entered table, including missing values. A draw does not guarantee representativeness. The same seed reproduces selection; it cannot recover cases outside the table.

Make a prediction and analyze the table.

Virtual bag: model with replacement

There are four equally likely tokens; choose how many are blue. Each token returns before the next draw. This is a deterministic simulation, without prizes, money or real observations.

Repeating the seed repeats the sequence. More draws do not guarantee a closer result in every run and do not validate the real-world model.

Two rules, the same accuracy

Ten fictional cases: nine normal and one failure. Rules defined for this example; no AI training or real equipment testing. Same number correct, different consequences.

Two rules, the same accuracy
RuleCorrect / totalMissed failuresFalse alarms

Your investigation report

Official links and next step

✦
YOUR MISSION

Distinguish association, confounding factors and comparison design.

Now it's your turn

Discovery workshop

Predict, check, explain

Distinguish association, confounding factors and comparison design.
Build your answers and check when you are ready.
A moment to understand

What conclusion fits observing faster A deliveries on one shift without controlling loads?

I need a hint

A good report separates observation, interpretation and causal hypothesis. A clearly stated limitation improves the conclusion.

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Take it with you

Sketch a fictional comparison of two rover settings. Define outcome, comparable conditions, assignment and one uncontrolled factor. Write an association statement and a causal hypothesis, distinguishing them.

At your own pace

My files and GitHub

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