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Week 1 · Experiment: Repeatability and fair tests

Session 3 of 4 · Meet your robot · Phase 1

Plan about 15 minutes for explanation, 30 minutes for practical work and 10–15 minutes for documentation. A longer build may continue into the next session: stop safely, commit the current state and record the next check. Desktop simulations count as software evidence; label them clearly and record physical validation separately.

Engineering challenge

What can your robot actually measure, and which claims still need evidence? This session focuses on repeatability and fair tests.

Before you start

Week 1 hardware orientation and a notebook; no prior programming is assumed.

Equipment: Desktop Python, editor, paper and ruler; for physical work, the configured 3pi+ 2040, clear floor mat and hardware checklist. Week 3 additionally uses the separate low-voltage LED circuit described in its procedure.

For any motion, verify the stop button, short time limit and clear floor area first. Keep the wheels raised for a new device program until its commands and stop behaviour are checked. A hazard or uncertain input is a reason to stop and document, not to force the trial to finish.

Theory and mathematics

Repeatability and fair tests

Repeatability means obtaining comparable results under the same procedure. It does not mean every measurement must be identical. Starting angle, wheel slip and measurement timing can change outcomes. Choose one independent variable; record the dependent measurement; hold other conditions fixed. Keep failed trials with notes. If you discard failures after seeing them, your average describes selected successes rather than the actual system. A pilot is a preliminary run used to choose a safe procedure; label it separately from the final dataset.

Worked example — illustrative values

For fictional distances 19, 20, 20, 21 and 20 cm, the mean is (19 + 20 + 20 + 21 + 20)/5 = 20 cm. The range is 21 − 19 = 2 cm. Travelling 0.20 m in 1.0 s gives 0.20 m/s. This average says nothing about the speed at each instant.

Write the calculation in your notebook before running code. State which values you measured, which you assumed and which the program calculates. A correct numerical calculation cannot rescue an incorrect physical assumption.

Run and explain the model

The following is desktop Python, not a ready-to-run motor program. Download this week’s example, save it in your student repository and run python3 code/w01.py from the repository root. The same small model is reused across the week so you can learn it, build with it, test it and revise it.

# Desktop Python teaching example. Numerical inputs are illustrative.
distances_cm = [19, 20, 20, 21, 20]
print("mean_cm", sum(distances_cm) / len(distances_cm))
print("range_cm", max(distances_cm) - min(distances_cm))

Use your own recorded data or named test fixtures instead of the illustrative inputs. Save expected and actual values side by side. If an exception appears, read its final line, identify the input or assumption that caused it and make the smallest explained correction. Do not delete validation merely to obtain output.

Understanding the model and its limits

The list holds five distance observations in centimetres. len counts them, sum adds them, and division produces their mean. max and min select endpoints of the observed spread. Neither calculation estimates a cause. Replacing the last value with 30 cm changes the mean and range: work out both before running. Retain that value if it was genuinely measured; first investigate whether it represents a transcription error, a setup change or actual unusual motion. The calculation is simple enough to check independently, which is the point of the first experiment.

Practical instructions

  1. Use the hardware guide to verify the button-C stop and bounded pulse with wheels raised.
  2. Mark a clear floor lane; pilot one short, low-command pulse, with an adult available to switch off power.
  3. Freeze command and duration; reset the same starting pose for five trials and measure distance/time.
  4. Save trial, command, duration_s, distance_m, time_s and notes as CSV, retaining failures.

Experiment

Test the hypothesis that five fixed pulses stay within a ±2 cm target band around a distance chosen in the pilot. This is a teaching tolerance, not a vendor specification.

Before testing, record your prediction, changed factor, measured response, fixed conditions and stopping rule. Save every attempted run, including failures, with a condition and source version. If hardware is unavailable, use an explicitly labelled synthetic/replay dataset and list the physical question it cannot answer. Do not invent completed trials.

Deliverable

Five recorded trials or a documented safety stop; units and a predeclared tolerance; explain one uncontrolled factor.

Save notebook/w01-s3.md, the relevant code revision, raw CSV or test-case records, and one labelled diagram/plot/table. Link the files relatively from your notebook. Use the entry template and report guide.

Completion criteria

A documented failed prediction can meet the learning criteria. A missing physical trial must remain marked untested; software success alone does not validate the robot.

Reading and video

Reflection and next step

Which assumption most affected your result? Point to one observation that supports your explanation and one alternative explanation the evidence has not ruled out. Write a specific next test with a changed factor and measurable outcome, then proceed through the week’s Learn → Build → Experiment → Improve cycle.

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