Safety and evidence boundary
This week emphasizes supplied datasets, simulations, and design reasoning. Do not improvise sealed-organism chambers, temperature stress, chemical gas absorbers, or invasive measurements. Use only teacher-approved procedures under appropriate supervision.
Explicit science instruction
A follow-up investigation begins with a pattern that needs explanation. The design should specify the question, focal variable, response variable, controls, sample or simulation structure, number of replicates, measurement interval, analysis method, and stop/safety conditions.
Good design also plans for interpretation before data collection. What result would support the prediction? What result would challenge it? What confounder could mimic the effect? What measurement limitation could conceal a real effect?
For this course, students may design a conceptual experiment without carrying it out. A supplied dataset or simulation can provide authentic evidence-analysis practice without unsafe manipulation of living organisms.
Vocabulary: hypothesis; operational definition; replicate; control; confounder; prediction; analysis plan; stop condition.
Worked example with reasoning
Observation: a supplied dataset shows higher normalized respiration proxy at Condition B than A. Follow-up question: does temperature explain the difference? Strong design: use equal sample masses or normalize by mass, keep substrate and measurement time consistent, include multiple replicates, use approved temperature conditions or simulation, measure the same proxy with the same calibrated method, and predefine the comparison. Weak design: compare one warm large sample to one cool small sample.
Guided question
What result would make you revise the hypothesis that temperature caused the original difference?
Independent work
- Write one testable follow-up question.
- Define independent and dependent variables operationally.
- List at least four controlled or recorded variables.
- Specify replication and analysis.
- Write one safety/stop condition and one supplied-data alternative.
Misconception check
A detailed procedure is not automatically a strong experiment. If the comparison cannot isolate the focal variable or the measurement does not answer the question, adding more procedural steps does not fix the logic.
Formative understanding check
Why should an analysis plan be considered before data collection?
Show the worked response
It helps ensure the measurements actually address the question, reduces opportunistic interpretation after seeing results, and clarifies what comparisons, units, normalization, and uncertainty information are needed.
Open-book formative practice only; not an official mastery attempt or grade.
Mentor evidence conference
Ask Archaemenes to challenge your interpretation: What was measured directly? What process was inferred? Which confounder, normalization choice, or uncertainty could change the conclusion? Revise the claim yourself.
Open Archaemenes for this lessonTeacher guidance
Foundation: use a structured design organizer. Core: require variables, controls, replication, analysis, and safety alternative. Extended: ask students to compare two competing designs and defend which produces stronger causal evidence.
Preserve learning evidence
Keep the calculations, graph or design, uncertainty statement, correction notes, and explanation in the matching Week 12 evidence field.
Return to thursday Evidence Workspace